Field of the Invention The present invention relates to advancement in process and an intelligent unified framework for colour object analysis in a scene in order to develop efficient video analytics applications and other intelligent machine vision technologies. The advancement is directed to provide an intelligent and adaptive framework for improved colour object detection method which can eliminate the defects encountered in the presently available techniques of colour object detection irrespective of any video noises like shadow, glare, colour changes due to varying illumination, and effect of lighting condition on colour appearance, electronics generated induced noises (e.g. shot noise, but not limited to) and other type of noises sensitive to human vision system. Advantageously, object analysis technique is also capable of detecting and characterizing static objects along side with colour moving objects in the same scene by an advanced unified framework based on multi-layer estimation technique. Background of the Invention Video Management Systems are used for video data acquisition and search processes using single or multiple servers. They are often loosely coupled with one or more separate systems for performing operations on the acquired video data such as analyzing the video content, etc. Servers can record different types of data in storage media, and the storage media can be directly attached to the servers or accessed over IP network. This demands a significant amount of network bandwidth to receive data from the sensors (e.g, Cameras) and to concurrently transfer or upload the data in the storage media. Due to high demand in bandwidth to perform such tasks, especially for video data, often separate high speed network are dedicated to transfer data to storage media. Dedicated high speed network is costly and often require costly storage devices as well. Often this is overkill for low or moderately priced installations. It is also known that to back up against server failures, one or more dedicated fail- over (sometimes called mirror) servers are often deployed in prior art. Dedicated fail- over servers remain unused during normal operations and hence resulting in wastage of such costly resources. Also, a central server process either installed in the failover server or in a central server is required to initiate the back-up service, in case a server stops operating. This strategy does not avoid a single point of failure. Moreover, when the servers and clients reside over different ends in an internet and the connectivity suffers from low or widely varying bandwidth, transmission of multi- channel data from one point to another becomes a challenge. Data aggregation techniques are often applied in such cases which are computationally intensive or suffer from inter-channel interference, particularly for video, audio or other types of multimedia data. As regards analytic servers presently in use it is well known that there are many video analytics system in the prior art. Video content analysis is often done per frame basis which is mostly pre defined which make such systems lacking in desired efficiency of analytics but are also unnecessarily cost extensive with unwanted loss of valuable computing resources. Added to the above, in case of presently available techniques of video analysis ,cases of unacceptable number of false alarms are reported when the content analysis systems are deployed in a noisy environment for generating alerts in real time. This is because the traditional methods are not automatically adaptive to demography specific environmental conditions, varying illumination levels, varying behavioural and movement patterns of the moving objects in a scene, changes of appearance of colour in varying lighting conditions, changes of appearance of colours in global or regional illumination intensity and type of illumination, and similar other factors. It has therefore been a challenge to identify the appearance of a non-moving foreign object (static object) in a scene in presence of other moving objects, where the moving objects occasionally occlude the static object. Detection accuracy suffers in various degrees under different demographic conditions. Extraction of particular types of objects (e.g. face of a person, but not limited to) in images based on fiduciary points is a known technique. However, computational requirement is often too high for traditional classifier used for this purpose in the prior art, e.g., Haar classifier. Also, in a distributed system where multiple sites with independent administrative controls are present, unification of those systems through a central monitoring station may be required at any later point of time. This necessitates hardware and OS independence in addition to the backward compatibility of the underlying computational infrastructure components, and the software architecture should accommodate such amalgamation as well. It would be thus clearly apparent from the above state of the art that there is need for advancement in the art of sensory input/data such as video acquisition cum recording and /or analytics of such sensory inputs/data such as video feed adapted to facilitate fail-safe integration and /or optimized utilization of various sensory inputs for various utility applications including event/alert generation, recording and related aspects. Automatic separation of foreground moving objects from the static background in an image sequence (video) is the primary task for subsequent analysis of video. These separated moving objects are the keys for any development on video analytics application. Efficient execution of this task using colour video data that represents a dynamic scene is challenging and is of immense interest to the experts in the domain of intelligent machine vision technology and related applications. Foreground object extraction in a video is a primary requirement and several basic technologies are adopted by the experts in image processing and computer vision. Foreground object extraction can be treated as a background subtraction problem. That is in a video, foreground objects can be detected simply by subtracting the current image from a background image of the scene. This background image needs to be determined beforehand. Several approaches have been proposed to estimate the background from a video sequence in literatures. However, if the background is consistently affected by shadow, glare, time varying noises, effect of lighting variation on colour, background estimation becomes a very challenging task especially in outdoor environment when different seasonal environment is always a concern. The goal of foreground object extraction is to divide an image into its constituent regions which are sets of connected pixels or objects, so that each region itself will be homogeneous with respect to the different physical objects whereas different regions will be heterogeneous with each other. The foreground object extraction accuracy may determine the eventual success or failure of many sub- sequent techniques for video analytics and object recognition, and object based different event-detection. The known art techniques suffered from imperfect generation of blobs incoherent with the actual size, shape, feature of the original object distinguishable by human eyes. In addition to estimation of proper background scene, another key challenge is also to handle the shadows and glares during foreground extraction process so that the objects can be detected accurately. Due to obvious presence of the natural phenomenon such as shadow and glare, appearance of the objects in the scene becomes distorted. As a result, the extracted foreground objects associated with the shadow and glare do not give the proper information about object features like position, size, shape, contour etc. and any sub-sequent techniques dependent on these object features bound to fail. In a real scenario, nature of the shadow and glare can be static, moving or both. Static or very slowly moving shadow and glare can be modeled by some background modeling techniques. But moving shadows and glares that are associated with moving objects are hard to model and eliminate from being detected. Hence effective identification of shadow and glare regions and elimination of those regions from actual foreground objects remain to be challenging and important for any video analytic applications. Traditionally, shadow and glare are detected using fixed thresholding methods where a set of fixed and trained thresholds are used to detect the shadow and glare regions. Mostly these fixed thresholds are derived by observing the variation of pixel intensity over video frames due to presence of shadow and glare in a specific type of scene, so their applicability is limited to that type of scene only. Some techniques improve the fixed thresholding approach by introducing a modeling on shadow and glare thresholds to make them adaptive, but till either they are very specific to type of the scene or they require a lot of computations. Another type of shadow detection approaches applies scene knowledge based object-wise shadow regions identification. These approaches use a scene knowledge (e.g. difference of shape, size, colour etc. between objects associated with shadow and without any shadow) about the appearance of shadows in the scene and apply that knowledge to identify and distinguish the shadow regions from the associated objects. However, accuracy of the said techniques is low when applied in real-life scenario where one scene varies widely with respect to other scene, and also with respect to time. Objects of the Invention It is thus the basic object of the present invention to provide an intelligent and adaptive framework for improved colour object detection method which can eliminate the defects encountered in the prior state-of-art irrespective of any video noises like shadow, glare, colour changes due to varying illumination, and effect of lighting condition on colour appearance, electronics generated induced noises (e.g. shot noise, but not limited to) and other type of noises sensitive to human vision system. Another object of the present invention is directed to advancements in sequence of processes adapted to provide more accurate information of colour objects in an image taken from any video sequence including by low cost cameras whereby any sequential video images can be processed with this method to locate all possible detectable colour objects and their related information which can be further processed to analyze the scene dynamics with respect to the object itself and in association with other foreground objects wherein the extracted information can be used to measure any statistical information regarding the object or association of the colour object with any other animate or inanimate colour objects in the scene. Yet another object of the present invention is directed to a method for improved colour background information by eliminating the defects encountered in the prior state-of-art in presence of video noises like spatial movement of non-meaningful objects, change of appearance of colour due to presence of shadow, change of appearance of the colour in the object when it moves to a low intensity (darker) region from a higher intensity (brighter) region and vice versa. Another object of the present invention is directed to a technique which would be adaptive also when the colour appearance of the foreground objects and background of the scene changes frame to frame due to change in global intensity or other phenomena such flickering, sensitivity of the sensor in the camera, etc. Yet another object of the present invention is directed to an object analysis technique adapted for detecting and characterizing static objects along side with colour moving objects in the same scene by way of an advanced unified framework based on multi-layer estimation technique. A further object of the present invention is directed to a multi-layer static foreground pixel estimation technique overcoming the inability of any traditional background estimation technique to distinguish the background pixels from the foreground pixels that remain static for a long duration which would further enable . better control over the process of distinguishing the static foreground pixels from the background. Another object of the present invention is directed to advancements in method discussed above by interconnecting a number of intelligent components consisting of hardware and software, and involving implementation techniques adapted to make the system efficient, scalable, cost effective, fail-safe, adaptive to various demographic conditions, adaptive to various computing and communication infrastructural facilities. Summary of the Invention Thus according to the basic aspect of the present invention there is provided an intelligent and unified method of multiple component colour object analysis in a scene favouring scene analytic applications comprising: multiple component colour coherent background estimation involving colour correlation of neighbouring pixels and inter-frame multiple component colour correlation using said multiple components as a composite data and using the relative values of these components to maintain accurate colour information and appearance of the true colour in the estimated background frame. An intelligent and unified method as above wherein said multiple components comprise multi-spectraI signals including human visible spectra Red (R), Green (G), Blue (B) signals and similar. An intelligent and unified method of colour object analysis as above comprising (A) unified colour coherent background estimation involving statistical pixel processing;(B) removal of shadow and glare from the scene alongwith removal of electronics induced different types of noises in sensors and vibrations of sensors;(C) characterization of pixels in the foreground regions and extract moving and/or static objects. An intelligent and unified method of colour object analysis as above comprising tracking variety of objects individually and generating related information for rule- engine based intelligent analytical applications. An intelligent and unified method of colour object analysis as above wherein said unified colour coherent background estimation involving statistical pixel processing comprises using R,G,B components as a composite single structure in a unified manner to thereby preserve the mutual relationship of theses colours components in each individual pixel in order to maintain true colour appearances in the estimative colour background frame; continuously readjusting estimated or predicted values for each colour pixel in a frame with all sequential forthcoming frames of the colour video; correlate the spatial distribution of the colour values in a local region to model the pixel background colour value. An intelligent and unified method of colour object analysis as above wherein for each pixel (x,y) in the input colour frame there is carried out (i) local window estimation (ii) colour analysis of each pixel and (iii) background frame construction based thereon. An intelligent and unified method of colour object analysis as above wherein if the pixel location in a current frame belongs to an object pixel in the previous frame, estimation of colour background at that pixel location is skipped since the colour pixel is not representative of the background estimation ,otherwise, compute an adaptive size (k * h, k* w) local window centering around this pixel for computation of the background estimation using the colour pixel values within this window, where representing normalized average intensity of all the pixels in window size (h, w). for all 0