Claims:1. A reference image retrieval system for undercarriage image of the vehicle, the system comprising:
an imaging sensor configured to capture the undercarriage image of the vehicle, wherein the imaging sensor consists of optical cameras;
an image acquisition unit to crop the images captured by imaging sensor based on ROI (region of interest) and stitching unit to stitch all the images into single high quality composite image;
a database with plurality of reference undercarriage images of vehicle;
an image normalization block to bring the query (captured) undercarriage image and reference undercarriages image into the same intensity levels, wherein the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image;
a query feature matching unit for matching the query undercarriage image with the reference undercarriage image, wherein the matching further comprising of repeating the procedure of image normalization if the query image is NOT matched with the reference undercarriage image given from the database; and
a display unit to display the retrieved reference undercarriage image if the query (captured) undercarriage image is matched with the reference undercarriage image given from the database.

2. The system as claimed in claim 1, wherein the image acquisition unit acquires the image and crops the image based on ROI (region of interest) with respect to positioning of optical camera.

3. The system as claimed in claim 1, wherein the stitched undercarriage image (query image) of the vehicle and the reference undercarriage image from the database are used as an input data to the image normalization block.

4. The system as claimed in claim 1, wherein reference undercarriage image and query undercarriage image were normalized and fed to the query feature matching block.

5. The system as claimed in claim 1, wherein the retrieving block is used to identify retrieving, and selecting the reference undercarriage image from the database using query feature matching unit.

6. The method for reference image retrieval for the undercarriage image of the vehicle, the method comprising the steps of:
capturing the query undercarriage of the vehicle using imaging sensor, wherein the imaging sensor consists of optical cameras which is mounted on the fixed or movable platform;
stitching all the captured query undercarriage images into single high quality composite image by image stitching unit and crops the image based on ROI (region of interest) captured by the imaging sensor;
converting the color undercarriage image of vehicle in to the grayscale image by an RGB to gray conversion unit and;
selecting ROI from an image for process by an image acquisition unit;
loading a reference undercarriage image from the database for matching with the query undercarriage image;
converting the color reference image in to the grayscale image which is in the database by RGB to gray conversion unit ;
normalizing the query undercarriage image and reference undercarriage image from database in to the same contrast and intensity level by image normalization block, wherein the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image; and
matching the query undercarriage image with the reference undercarriage image from database by query feature matching unit further comprising of:
repeating the procedure of image normalization and query feature matching if the query image is NOT matched with the reference image given from the database; and
displaying the retrieved undercarriage image on display unit if the query image is matched with the reference image given from the database.

7. The method as claimed in claim 1, wherein stitched undercarriage image (query image) of the vehicle is used as an input data to the image normalization block.

8. The method as claimed in claim 1, wherein reference undercarriage image and query undercarriage image were normalized and fed to the query feature matching block.

9. The method as claimed in claim 8, wherein the query feature matching method further comprises of:
finding the resolutions of the query and reference undercarriage images;
finding the convolution time and FFT time between query and reference undercarriage images;
convoluting in spatial domain if the convolution time less than FFT time between query and reference undercarriage images;
correlating in frequency domain if the convolution time greater than FFT time between query and reference undercarriage images;
calculating local sums of the convoluted or correlated output;
subtracting the local arrays with the local sum;
assigning correlation coefficients where output array has zero variance;
finding the maximum peak of the output array; and
checking the match based on the peak positions and the maximum value of the array.

10. The method as claimed in claim, wherein the retrieving the reference image block is used to identify and retrieve the reference image from database using query feature matching unit and the display unit is used to render the outcome of the retrieving the reference image block.
, Description:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)

“A reference image retrieval system and method”
By
Bharat Electronics Limited,
Central Research Laboratory
Jalahalli P.O., Bangalore – 560013

The following specification particularly describes the invention and the manner in which it is to be performed.

Field of the invention
The present invention mainly relates to the image retrieval and more particularly to the reference image retrieval system and method for the undercarriage image of the vehicle.
Background of the invention
An image retrieval system is well known in the art which is a computer system for browsing, searching and retrieving images from a large database of digital images. The image retrieval systems are of importance for applications that involve large collections of images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning', keywords, or descriptions to the images so that retrieval can be performed.
Basically, the image retrieval system comprises a database with a large number of images. A user searching for a particular image specifies a query image as to how the retrieved image or images should look like. Then the system compares the stored images with the query image and ranks the stored images according to their similarity with the query image. The ranking results are presented to the user who may retrieve one or more of the images. The comparison of the query image with a stored image to determine the similarity may be based on a number of features derived from the respective images. The image feature or features used for comparison are called a feature vector i.e. colour histogram.
The prior art discloses the different methods of retrieving objects from the database. Different methods of localization and identification of objects are also describes.
Document EP2405391A1 describes an image retrieval method comprising: a step of extracting at least one query feature vector from a query image on which a subject of the image retrieval is captured, the query feature vector representing a local feature of the query image; a step of accessing an image data base in which a plurality of reference images are stored previously, each reference image being stored in conjunction with learning images generated there from and reference feature vectors representing local features of the reference image and the learning images; a comparing step of comparing the query feature vector with the reference feature vectors stored in conjunction with each reference image using an approximate nearest neighbor search to find a reference feature vector approximately nearest to the query feature vector; and a selecting step of selecting a reference image with which found reference feature vector is stored in conjunction from the reference images as a retrieval result wherein: the learning image is generated by adding a defocus and/or a motion-blur effect likely to occur on capturing the subject to each reference image, the reference feature vectors are extracted from each reference image and the learning image corresponding to the reference image respectively using the scale-space approach, the query feature vector is extracted from the query image using the scale-space approach, and each of the above steps is executed by a computer.
Another, document US8306281B2 describes a similar face retrieval system for retrieving an image photographing a face similar to a face detected from a retrieval query image from a retrieval target image group by using an image photographing a human face as the retrieval query image, whole image features as features representative of background information are extracted from each whole area of an each image of a retrieval target image group, to calculate a degree of similarity through comparison with each set of whole image features, and an image having a degree of similarity not lower than certain value and having a lower retrieval result order from retrieval results. It is possible to efficiently retrieve the same person playing different scenes by utilizing different features for a retrieval process and filtering process.
Further, document US6665442 B2 describes when a retrieval condition of an attribute list is input from a user interface unit to a retrieval processing unit, the attribute list stored in an attribute list storing unit is retrieved in the retrieval processing unit. Thereafter, attribute information conforming to the retrieval condition is output to and displayed on a displaying unit. Thereafter, when a retrieval condition of the similarity retrieval is input from the user interface unit to the retrieval processing unit, image data stored in the image information storing unit is retrieved in the retrieval processing unit, and specific image data relating to a characteristic descriptor set conforming to the retrieval condition is selected in the retrieval processing unit. Thereafter, the specific image data is output to and displayed on the displaying unit.
Further, document US6859552B2 describes an image retrieving apparatus for retrieving a retrieving image in an input image, a color histogram of an image in a retrieving area in the input image is compared with a color histogram of the retrieving image. At first, a candidate area in which the retrieving image can be included is roughly retrieved by rough image retrieving with selecting a larger retrieving area and rough resolution of gradation of the histograms. Subsequently, an area including an image corresponding to the retrieving image is precisely retrieved by fine image retrieving with a smaller retrieving area and fine resolution of gradation of the histograms.
Also, in document US20030021481A1 describes an object of the present invention is to provide an image retrieval system for computing similarity in the case when components are different between an image feature vector of a query image and the image feature vectors of images to be retrieved when retrieval is carried out by using features of images. A first image feature vector conversion device converts a query image feature vector into a first image feature vector for used in a similarity computation according to similarity computation image feature vector configuration information describing a given configuration of an image feature vector for use in a similarity computation. A second image feature vector conversion device converts images to be retrieved feature vector into a second image feature vector for use in a similarity computation according to the similarity computation image feature vector configuration information. The image feature vector similarity computation devices compare the first image feature vector with the second image feature vector according to the similarity computation image feature vector configuration information, and compute the similarity.
In a practical set up, the number of images can be very large. On the database for example, the number of images can be of the order of millions and is ever growing. Even if the time to compare the query image with an applicant image is very short, the cumulative time needed to compare the query image with all images in the database will be long. It is a drawback of the known system that a user searching for an image in such a large database must wait a long time after having submitted the query image in the system.
Therefore there is a need in the art with the novel reference image retrieval scheme to solve the above mentioned limitations.
Summary of the Invention
An aspect of the present invention is to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below.
Accordingly, in one aspect of the present invention relates to a reference image retrieval system for undercarriage image of the vehicle, the system comprising: an imaging sensor configured to capture the undercarriage image of the vehicle, where the imaging sensor consists of optical cameras which is mounted on the movable platform, an image acquisition unit to crop the images captured by imaging sensor based on ROI (region of interest) and stitching unit to stitch all the images into single high quality composite image, a database with plurality of reference undercarriage images of vehicle, an image normalization block to bring the query (captured) undercarriage image and reference undercarriages image into the same intensity levels, where the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image, a query feature matching unit for matching the query undercarriage image with the reference undercarriage image, where the matching further comprising of repeating the procedure of image normalization if the query image is NOT matched with the reference undercarriage image given from the database and a display unit to display the retrieved reference undercarriage image if the query (captured) undercarriage image is matched with the reference undercarriage image given from the database.
Another aspect of the present invention relates to a method for reference image retrieval for the undercarriage image of the vehicle, the method comprising the steps of: capturing the query undercarriage of the vehicle using imaging sensor, wherein the imaging sensor consists of optical cameras which is mounted on the fixed or movable platform, stitching all the captured query undercarriage images into single high quality composite image by image stitching unit and crops the image based on ROI (region of interest) captured by the imaging sensor, converting the color undercarriage image of vehicle in to the grayscale image by an RGB to gray conversion unit and selecting ROI from an image for process by an image acquisition unit, loading a reference undercarriage image from the database for matching with the query undercarriage image, converting the color reference image in to the grayscale image which is in the database by RGB to gray conversion unit, normalizing the query undercarriage image and reference undercarriage image from database in to the same contrast and intensity level by image normalization block, wherein the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image, matching the query undercarriage image with the reference undercarriage image from database by query feature matching unit further comprising of: repeating the procedure of image normalization and query feature matching if the query image is NOT matched with the reference image given from the database and displaying the retrieved undercarriage image on display unit if the query image is matched with the reference image given from the database.
Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.
Brief description of the drawings
The above and other aspects, features, and advantages of certain exemplary embodiments of the present invention will be more apparent from the following description taken in conjunction with the accompanying drawings in which:
Figure 1 illustrates the block diagram of reference undercarriage image retrieval system according to one embodiment of the present invention.
Figure 2 illustrates the flow chart describing the reference undercarriage image retrieval process according to one embodiment of the present invention.
Figure 3 illustrates the flow chart describing the query image matching process with the database of reference undercarriage images according to one embodiment of the present invention.
Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may have not been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.
Detailed description of the invention
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces.
By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic was intended to provide.
Figs. 1 through 3, discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged systems. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions, in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
The present invention provides a new way of matching and retrieving the vehicle undercarriage image from the reference undercarriage images stored in the database. A novel method for image normalization of query and reference undercarriage images in the database and multi level feature matching between query and reference undercarriage for retrieving the reference image is claimed. Normalization of query and reference image is essential to bring the both images into the same contrast and intensity levels. After normalization, a specialized technique is used to match the query image features with the reference image features. Check for accurate match using rule based condition to detect the exact match between query undercarriage image and reference undercarriage images. Once match found, retrieve the reference image from the database. Thus the present method and system intelligently processes the query undercarriage image of the vehicles and matches the features with the reference undercarriage images stored in the database and then retrieves the most suitable reference undercarriage image.
Figure 1 illustrates the block diagram of reference undercarriage image retrieval system according to one embodiment of the present invention.
The figure is a conceptual diagram of a reference image retrieval system in accordance with one embodiment of the present invention. The system comprises of imaging sensor 1, image acquisition and stitching unit 2, image normalization unit 3, database which has reference undercarriage images 4, query feature matching 5, retrieving from the database 6, and display unit 7.
The present invention, reference image retrieval system comprises of four main blocks. First block is image acquisition and stitching block which acquires the undercarriage snapshots of the vehicle and stitches those images to get a full view of the undercarriage image. Second block is image normalization block, this equalizes the intensity and contrast levels of the query and reference images. Third block is query image feature matching block which extracts the features from the query image and matches those on the reference images stored in the database. Finally, fourth block retrieves the reference undercarriage image from the database where the exact match occurred.
In the present invention the imaging sensor 1 is used to capture the images of the vehicle’s undercarriage and the image stitching unit 2 is used to stitch the captured images to make a high quality composite image. The present invention provides an imaging sensor where the sensors comprising of optical cameras, the sensor is a Progressive Color Area Scan-GigE camera 1. The image sensor is an industrial grade camera which will operate in adverse temperature. In addition, the camera provides exceptional resolution, thereby allowing the foreign object retrieval system to consider the fine details of an undercarriage of the vehicle. The camera or imaging sensor may be mounted on a fixed or movable platform. Image acquisition unit, acquires the image data and selects or crops the image based on ROI (region of interest) with respect to where the camera is mounted. The present invention captures the undercarriage image slices based on the vehicle presence detection sensor input trigger and process the undercarriage slices to make a full length undercarriage image. The stitching unit mosaics the cropped undercarriage images into a single high quality underside of the vehicle. The stitched undercarriage image is the query image to the reference image retrieval system.
The image normalization block 3 is used to bring the query image and reference image into the same intensity levels, which is helpful to register the both query and reference undercarriage images for further process. Normalization is done by adjusting the histogram of scanned undercarriage image in to the histogram of the reference image. This process will help in finding the accurate match between the query image and reference images.
Further, the present invention system includes a database of reference undercarriage images are stored in the database container 4. The matching technique is most critical to match the features present in the query image and the reference undercarriage images. The query feature matching element 5 finds the exact match between the query and reference undercarriage images. The normalization approach uses normalized cross-correlation of query undercarriage image and reference undercarriage image to find the accurate match. This matching method is generalized for two dimensional gray scale images. This method also uses either spatial or frequency domain cross-correlation based on convolution time estimated between the query undercarriage and reference undercarriage images.
Further the system includes retrieving the reference undercarriage image which is matched with the query image from database. Finally, the display unit 7 is used to show the outcome of the entire process. The display unit displays the query and reference undercarriage images on the screen is presented. This way, the reference undercarriage image retrieval from the database was performed based on the queried input undercarriage image.
Figure 2 illustrates the flow chart describing the reference undercarriage image retrieval process according to one embodiment of the present invention.
The figure 2 illustrates the flow chart describing the reference undercarriage image retrieval process. Initial step includes, capturing the query undercarriage of the vehicle using imaging sensor, wherein the imaging sensor consists of optical cameras which is mounted on the fixed or movable platform.
At next step, stitching all the captured query undercarriage images into single high quality composite image by image stitching unit and crops the image based on ROI (region of interest) captured by the imaging sensor.
After stitching, converting the color undercarriage image 11 of vehicle in to the grayscale image 12 by an image acquisition unit and selecting ROI 13 from an image for process by an image acquisition unit. Further, before normalization loading of the reference undercarriage image 15 from the database 14 for matching with the query undercarriage image and then converting the color reference undercarriage image into grayscale image16 which is in the database.
At step 17, normalizing the query undercarriage image and reference undercarriage image from database in to the same contrast and intensity levels by image normalization block, where the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image.
At step 18(A), matching the query undercarriage image with the reference undercarriage image from database by query feature matching unit further comprising of: repeating the procedure of image normalization and query feature matching if the query image is NOT matched 19 with the reference image given from the database and displaying the retrieved undercarriage image 20 on display unit if the query image is matched with the reference image given from the database. In the present invention the stitched undercarriage image (query image) of the vehicle and the reference undercarriage image from database are used as an input data to the image normalization block. The reference undercarriage image and query undercarriage image were normalized and fed to the query feature matching block.
Figure 3 illustrates the flow chart describing the query image matching process with the database of reference undercarriage images according to one embodiment of the present invention.
The figure illustrates the flow chart describing the query image matching process with the database of reference undercarriage images. The query matching process includes several steps:
At step 21, the query matching process finds the resolutions of the query and reference undercarriage images.
At step 22 finds the convolution time and FFT time between query and reference undercarriage images.
At step 23 convoluting in spatial domain 24 if the convolution time less than FFT time between query and reference undercarriage images and correlating in frequency domain 25 if the convolution time greater than FFT time between query and reference undercarriage images.
At step 26, calculates local sums of the convoluted or correlated output. At step 27, subtracts the local arrays with the local sum. At step 28, assigns correlation coefficients where output array has zero variance. At step 29, finds the maximum peak of the output array and at step 30 checks the match based on the peak positions and the maximum value of the array.
Advantages of the present invention
The present invention system and method is robust retrieval approach.
The present invention “reference image retrieval system and method is very accurate and reduces overall time.
Less complexity, fast and has application in homeland security.
Those skilled in this technology can make various alterations and modifications without departing from the scope and spirit of the invention. Therefore, the scope of the invention shall be defined and protected by the following claims and their equivalents.
FIGS. 1-3 are merely representational and are not drawn to scale. Certain portions thereof may be exaggerated, while others may be minimized. FIGS. 1-3 illustrate various embodiments of the invention that can be understood and appropriately carried out by those of ordinary skill in the art.
In the foregoing detailed description of embodiments of the invention, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description of embodiments of the invention, with each claim standing on its own as a separate embodiment.
It is understood that the above description is intended to be illustrative, and not restrictive. It is intended to cover all alternatives, modifications and equivalents as may be included within the spirit and scope of the invention as defined in the appended claims. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively.

We Claim:

1. A reference image retrieval system for undercarriage image of the vehicle, the system comprising:
an imaging sensor configured to capture the undercarriage image of the vehicle, wherein the imaging sensor consists of optical cameras;
an image acquisition unit to crop the images captured by imaging sensor based on ROI (region of interest) and stitching unit to stitch all the images into single high quality composite image;
a database with plurality of reference undercarriage images of vehicle;
an image normalization block to bring the query (captured) undercarriage image and reference undercarriages image into the same intensity levels, wherein the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image;
a query feature matching unit for matching the query undercarriage image with the reference undercarriage image, wherein the matching further comprising of repeating the procedure of image normalization if the query image is NOT matched with the reference undercarriage image given from the database; and
a display unit to display the retrieved reference undercarriage image if the query (captured) undercarriage image is matched with the reference undercarriage image given from the database.

2. The system as claimed in claim 1, wherein the image acquisition unit acquires the image and crops the image based on ROI (region of interest) with respect to positioning of optical camera.

3. The system as claimed in claim 1, wherein the stitched undercarriage image (query image) of the vehicle and the reference undercarriage image from the database are used as an input data to the image normalization block.

4. The system as claimed in claim 1, wherein reference undercarriage image and query undercarriage image were normalized and fed to the query feature matching block.

5. The system as claimed in claim 1, wherein the retrieving block is used to identify retrieving, and selecting the reference undercarriage image from the database using query feature matching unit.

6. The method for reference image retrieval for the undercarriage image of the vehicle, the method comprising the steps of:
capturing the query undercarriage of the vehicle using imaging sensor, wherein the imaging sensor consists of optical cameras which is mounted on the fixed or movable platform;
stitching all the captured query undercarriage images into single high quality composite image by image stitching unit and crops the image based on ROI (region of interest) captured by the imaging sensor;
converting the color undercarriage image of vehicle in to the grayscale image by an RGB to gray conversion unit and;
selecting ROI from an image for process by an image acquisition unit;
loading a reference undercarriage image from the database for matching with the query undercarriage image;
converting the color reference image in to the grayscale image which is in the database by RGB to gray conversion unit ;
normalizing the query undercarriage image and reference undercarriage image from database in to the same contrast and intensity level by image normalization block, wherein the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image; and
matching the query undercarriage image with the reference undercarriage image from database by query feature matching unit further comprising of:
repeating the procedure of image normalization and query feature matching if the query image is NOT matched with the reference image given from the database; and
displaying the retrieved undercarriage image on display unit if the query image is matched with the reference image given from the database.

7. The method as claimed in claim 1, wherein stitched undercarriage image (query image) of the vehicle is used as an input data to the image normalization block.

8. The method as claimed in claim 1, wherein reference undercarriage image and query undercarriage image were normalized and fed to the query feature matching block.

9. The method as claimed in claim 8, wherein the query feature matching method further comprises of:
finding the resolutions of the query and reference undercarriage images;
finding the convolution time and FFT time between query and reference undercarriage images;
convoluting in spatial domain if the convolution time less than FFT time between query and reference undercarriage images;
correlating in frequency domain if the convolution time greater than FFT time between query and reference undercarriage images;
calculating local sums of the convoluted or correlated output;
subtracting the local arrays with the local sum;
assigning correlation coefficients where output array has zero variance;
finding the maximum peak of the output array; and
checking the match based on the peak positions and the maximum value of the array.

10. The method as claimed in claim, wherein the retrieving the reference image block is used to identify and retrieve the reference image from database using query feature matching unit and the display unit is used to render the outcome of the retrieving the reference image block.

Abstract
The present invention relates to a reference image retrieval system for undercarriage image of the vehicle. In one embodiment, the system comprising: an imaging sensor configured to capture the undercarriage image of the vehicle, where the imaging sensor consists of optical cameras which is mounted on the fixed or movable platform, an image acquisition unit to crop the images captured by imaging sensor based on ROI (region of interest) and stitching unit to stitch all the images into single high quality composite image, a database with plurality of reference undercarriage images of vehicle, an image normalization block to bring the query (captured) undercarriage image and reference undercarriage images into the same intensity levels, where the normalization is done by adjusting the histogram of scanned undercarriage image into the histogram of the reference image, a query feature matching unit for matching the query undercarriage image with the reference undercarriage image, where the matching further comprising of repeating the procedure of image normalization and query feature matching if the query image is NOT matched with the reference undercarriage image given from the database and a display unit to display the retrieved reference undercarriage image if the query (captured) undercarriage image is matched with the reference undercarriage image given from the database.