CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present disclosure in general relates to the field of fault detection. More particularly, the present subject matter relates to a system and a method for fault detection in a traffic signal.
BACKGROUND
[003] Traffic signals are crucial for the proper regulation & smooth running of traffic at any crossing/ juncture in a city or town. It’s vital to have traffic signals work in a perfect condition to have traffic regulation at all these locations. However, faults in traffic signals may occur due to one or more reason like – traffic signal complete power failure, one or more signal lights getting off or fuse, timing tuning malfunction of signal timings and likewise.
SUMMARY
[004] Before the present system and a method for detecting a fault in traffic signal are described, it is to be understood that this application is not limited to a particular system, systems, and methodologies described, as there can be multiple possible embodiments, which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations, versions, or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and a method for detecting a fault in traffic signal. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one embodiment, a method for detecting a fault in traffic signal is disclosed. In the embodiment, the method comprises The method comprises obtaining an video stream associated with a traffic signal and generating data packets based on an image processing methodology and the video stream. In one example, the video stream is capture by a camera, and the traffic signal comprises one or more traffic light. In one other example, the data packets comprise at least a unique identifier and a current state of the traffic signal. The method further comprises generating an actual traffic signal model based on the data packets and detecting a fault in the traffic signal, based on a comparison of the actual traffic signal model with a predefined traffic signal model. In on example, the predefined traffic signal model may comprise a behavior model and a time model, and the predefined traffic signal model is indicative a correct functioning of the traffic signal.
[006] In one embodiment, a system for detecting a fault in traffic signal may be disclosed. The system comprises a memory and a processor coupled to the memory, further the processor may be configured to execute programmed instructions stored in the memory. In the embodiment, the system may obtain a video stream associated with a traffic signal. In one example, the video stream may be capture by a camera, and the traffic signal may comprise one or more traffic light. Further, the system may, generate data packets based on an image processing methodology and the video stream. In one example, the data packets may comprise at least a unique identifier and a current state of the traffic signal. Further, the system may generate an actual traffic signal model based on the data packets and detect a fault in the traffic signal, based on a comparison of the actual traffic signal model with a predefined traffic signal model. In on example, the predefined traffic signal model may comprise a behavior model and a time model, and the predefined traffic signal model is indicative a correct functioning of the traffic signal.
BRIEF DESCRIPTION OF DRAWINGS
[007] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present subject matter, an example of construction of the present subject matter is provided as figures; however, the present subject matter is not limited to the specific method and system disclosed in the document and the figures.
[008] The present subject matter is described in detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[009] Figure 1 illustrates a network implementation of a system for detecting a fault in traffic signal, in accordance with an embodiment of the present subject matter.
[0010] Figure 2 illustrates and embodiment of the system for detecting a fault in traffic signal, in accordance with an embodiment of the present subject matter.
[0011] Figure 3 illustrates a method for detecting a fault in traffic signal, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0012] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising," "having," "containing," and "including," and other forms thereof, are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system and method for detecting a fault in traffic signal , similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, system and method for detecting a fault in traffic signal are now described.
[0013] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments for detecting a fault in traffic signal. However, one of ordinary skill in the art will readily recognize that the present disclosure for detecting a fault in traffic signal is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[0014] As described, it is very critical to have traffic signals working in good condition in order to manage the traffic. The present subject matter addresses the problem and defines a method and system to monitor the status of traffic signals and detect faults. The present subject matter detects any flaw that occurs at a traffic signal setup in near real time. Though there are other methods to do the same, the present subject matter is based upon the camera vision based monitoring. Further, in the present subject matter, a set of cameras are deployed at each traffic juncture to detect any flaw that is caused by what so ever reason.
[0015] Exemplary embodiments for discussed above may provide certain more advantages. Further, in the subsequent description, embodiments of the present subject along with the advantages are explained in detail with reference to the Figures 1 to Figure 3.
[0016] Referring now to Figure 1, embodiment of a network implementation 100 of a system 102 for detecting a fault in traffic signal is disclosed. Although the present subject matter is explained considering that the system 102 is implemented on a server 110, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that multiple users may access the system 102 through one or more user device or applications residing on the user device 104-1….104-N. Examples of the user device may include, but are not limited to, a portable computer, a personal digital assistant, a handheld system, and a workstation. The system 102 may be communicatively coupled to gateway 114 and cameras 108-1...108-N through a network 106.
[0017] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may be either a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), Secure File Transfer Protocol (SFTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another.
[0018] In one embodiment, a system 102 for detecting a fault in traffic signal 112 may be disclosed. The system 102 comprises a memory and a processor coupled to the memory, further the processor may be configured to execute programmed instructions stored in the memory. In the embodiment, the system 102 may obtain a video stream associated with a traffic signal 112 and generate data packets based on an image processing methodology and the video stream. In one other example, the gateway 114 may obtain a video stream associated with a traffic signal and generate data packets based on an image processing methodology and the video stream. Further, upon generating the data packets the gateway 114 may transmit via the network 106 to the system 102 at a predefined interval such as every second. In one example, wherein the video stream may be capture by a camera 108, and the traffic signal 112 comprises one or more traffic light. In one more example, the data packets may comprise at least a unique identifier and a current state of the traffic signal. Further to the above, the system 102 may generate an actual traffic signal model based on the data packets and detect a fault in the traffic signal 112, based on a comparison of the actual traffic signal model with a predefined traffic signal model. In an example, predefined traffic signal model is configured at the backend server for each signal set-up in the city/town. In one example, the data packet is generated at a predefined time interval, such as every 30 seconds, or every 1 second. In on example, the predefined traffic signal model may comprise a behavior model and a time model, and the predefined traffic signal model is indicative a correct functioning of the traffic signal.
[0019] Upon detection, the system 102 may generate alert based on the fault. The system 102 may also determine a type of fault based on the comparison, such not working, wrong time cycle and generate a notification based on the type of the fault. In one example, the notification comprises data associated with a rectification of the fault, such as if the fault is lights not working, then carry spare lights.
[0020] Referring now to figure 2, an embodiment of the system 102 for detecting a fault in traffic signal is illustrated in accordance with the present subject matter. The system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any systems that manipulate signals based on operational instructions. Among other capabilities, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[0021] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing systems, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of systems to one another or to another server.
[0022] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0023] The modules 208 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include an obtaining module 212, a generation module 214, a detection module 216, a notification module 220, and other modules 224. The other modules 224 may include programs or coded instructions that supplement applications and functions of the system 102.
[0024] The data 210, amongst other things, serve as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a system data 226, and other data 228. In one embodiment, the other data 228 may include data generated as a result of the execution of one or more modules in the other module 224.
[0025] In one implementation, a user may access the system 102 via the I/O interface 204. The user may be registered using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing inputs, configuring or implementing the system 102.
[0026] In one embodiment, the receiving module 212 may obtaining a video stream associated with a traffic signal 112. In one example, the video stream may be captured by a camera 108, and the traffic signal 112 may comprise one or more traffic light. Further, the receiving module 212 may store the video stream in the system data 226.
[0027] Upon obtaining, the generation module 214 generate data packets based on an image processing methodology and the video stream and generate an actual traffic signal model based on the data packets. In one example the data packets may comprise at least a unique identifier and a current state of the traffic signal, and generated at a predefined time interval. Further, the generating module 214 may store the video actual traffic signal model in the system data 226.
[0028] Further to generating, the detection module 216 detecting a fault in the traffic signal based on a comparison of the actual traffic signal model with a predefined traffic signal model. Further, the detection module 214 may store the fault in the system data 226. In on example, the predefined traffic signal model may comprise a behavior model and a time model, and the predefined traffic signal model may be indicative a correct functioning of the traffic signal. In one example, behavior model may be sequenced of the light i.e. red, yellow green. In another example, time model may be the time duration of each light.
[0029] Upon detecting, the notification module 220 may generate alert based on the fault. Additionally, the notification module 220 may determine a type of fault based on the comparison and generate a notification based on the type of the fault. In one example, the notification may comprise data associated with a rectification of the fault. Further, the notification module 220 may store alert and notification in system data 226.
[0030] In the subsequent description one more example, of the present subject matter is disclosed.
[0031] In the example of the present subject matter a set of cameras 108 are mounted for each traffic signal, where the traffic signal comprise multiple traffic lights. Further, each of the traffic lights comprises at least two colors lights, red and green. In one example, the number of camera 108 on a road crossing may be equal to the traffic lights set up there. So if a two road crossing, there may be four cameras 108 mounted. While if it’s a T- point, there may be three cameras 108 mounted, and like wise. In one other example, in each traffic light set up – there may be three color lights – Red, Green & Yellow. Further, camera 108 monitoring a traffic light may be positioned opposite to it or in other words facing to it at a certain distance, for example could be fitted at the intersection crossing point over a pillar, or fitted along with traffic signal fixture itself. The camera 108 may be a video camera which may be able to collect and send video stream covering all three lights to the gateway 114. The gateway 114 may be an embedded system kind of processor based device which may have software stack to do the on-the-edge processing of video stream coming from monitoring cameras. Gateway 114 may have determine the status of each of the three color lights using image processing. For example, the gateway 114 may determine whether all three color lights are off, or is Red – ON, Green Off, Yellow Off, and like wise. Gateway may keep the processing in real time and send generate data packets at a defined interval for example, 1 second and transmit the data packets to the backend server. While sending the data packet to server, gateway may add required information on signal location, identity of road crossing (which may be uniquely configured by server), and status of each of the traffic signal light. Data may be sent in a pre-defined format for server could interpret the same.
[0032] Further in the example, Traffic signal problems are detected by comparing current state of a traffic signal with a predefined traffic signal model. In one example, predefined traffic signal model may comprises correct state of all the traffic signals to be monitored stored in the form of a model in the server 110. The predefined traffic signal model stored at server represents complete and accurate behavior of a traffic signal set up for complete cycle time. It represents state of all the lights in the traffic signal at each second of traffic signal cycle. The model is created/configured and stored for each of the traffic signal set up. That means if in a city there are 100 road crossings with traffic signal setup, there may be separate model for each of the 100 set ups. In one example, a predefined model definition is represented by an array of tuples. A tuple represents individual light state of all the lights facing a specific direction. There could be four states of a signal light - RED/YELLOW/GREEN/ NO ILLUMINATION.
[0033] An example traffic signal model for a road crossing where there are FOUR Traffic Signals that means 2 road crossing, is given below. Further, Table 1 illustrates the complete predefined model for the example.
TS1 [ (a,b,c, d), (a,b,c,d), (a,b,c,d), (a,b,c,d)] [Traffic signal state 1]
TS2 [ (a,b,c, d), (a,b,c,d), (a,b,c,d), (a,b,c,d)] [Traffic signal state 2]
……………………………………………...
TSn [ (a,b,c, d), (a,b,c,d), (a,b,c,d), (a,b,c,d)] [Traffic signal state n]
Where:
a, b, c, d: represent specific color of light at a given moment e.g. red / yellow / green / no illumination
Table 1:
Behavior Model Time Model
STAGE Stage 1 Stage 2 Stage 3 Stage 4 Time duration (Sec) Remarks
TS1 G R R R 15 Signal 1 stays green for 15 sec
TS2 Y R R R 4 Signal 1 stays yellow for 4 seconds
TS3 R G R R 15 Signal 2 stays green for 15 sec
TS4 R Y R R 4 Signal 2 stays yellow for 4 seconds
TS5 R R G R 20 Signal 3 stays green for 20 sec
TS6 R R Y R 4 Signal 3 stays yellow for 4 seconds
TS7 R R R G 20 Signal 4 stays green for 20 sec
TS8 R R R Y 4 Signal 4 stays yellow for 4 seconds

Total cycle time for one full cycle = 86

[0034] Number of tuples in an array may vary as per specific road intersection depending upon how many roads are meeting at the junction. Number of data elements in a tuple can also vary – 3/4/5/…. depending upon number of lights visible from a direction. Like in above example there are EIGHT TUPLES for a 2 Road Crossing with FOUR TRAFFIC SIGNALS.
[0035] Further in the example, each signal state as defined in point above may have an associated time t1 / t2 / .. tn which signifies that corresponding Signal State may persist for t1 / t2 / ... tn seconds before switching to another signal state. Say in our four traffic light signals example, first signal remains GREEN for 15 seconds, and then Yellow for 4 seconds before turning to RED. Hence Total cycle time for traffic signal Tm = t1+t2+...tn The Arrays / rows representing traffic signal states may be arranged in a circular buffer to reflect periodic repetition of signal states. Basically, circular buffer may have an array representing signal state corresponding to each second of total cycle time. So, there may be t1 instances of TS1 array, t2 instances of TS2 array and so on. Total number of arrays in the circular buffer may be Tm = t1+t2+...tn
[0036] The model defined above may be created and stored for each traffic signal to be monitored. Traffic signal monitoring gateway may upload the overall status data packet once every second, which may contain color /ON/OFF status of each of the traffic light. The backend model may hence be receiving the near real time status of overall traffic light set every second. Thus system 102 may be checking if the model as configured for that very intersection is being complied or not.
[0037] The system 102 may be receiving signal state data for each second on a continuous basis. This data may be populated in a temporary signal state array - TS [ (a,b,c, d), (a,b,c,d), (a,b,c,d), (a,b,c,d)] at the system 102. The populated array (actual model) may be used for comparison against the predefined model which stores the correct behavior of the traffic signal. Further fault may be detected if any of the configuration as created in model does not match with the actual value streaming from the signal setup in real time
[0038] In the example, during synchronization of the system for fault detection with the traffic signal, post rectification of the fault or during initial setup data collected in temporary Traffic Signal State Array may be compared at each light status level, with the data stored in model reflecting correct signal behavior. The Arrays in the model are compared in sequence one by one with the data in temporary signal state array. If no match is found with any of the stored signal states, it means there is some fault in the traffic signal and an alert is sent to appropriate authority using configured channels (email, SMS, pop up window on laptop/mobile) If a match is found, synchronization needs to be done between incoming signal stream and model which represents correct traffic signal states. For synchronization, next incoming signal data arrays are discarded until it’s different from last one. When it’s different, it’s compared with the first array of the next signal state of persistent circular buffer. If it matches, it means synchronization is done and system can move to steady state. If it doesn’t match, it means there is some fault in the traffic signal and an alert is sent to appropriate authority using configured channels (email, SMS, pop up window on laptop/mobile).
[0039] Exemplary embodiments for detecting a fault in traffic signal discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages, without limitation, are the following.
[0040] Some embodiments of the system and the method enable a simple and robust way of fault detection in a traffic signal.
[0041] Some embodiments of the system and the method enable detection of fault irrespective of the underlying reason.
[0042] Some embodiments of the system and the method require no modifications in the existing traffic signal equipment.
[0043] Some embodiments of the system and the method addition of equipment in non-intrusive way.
[0044] Referring now to figure 3, a method 300 for detecting a fault in traffic signal using a system 102, is disclosed in accordance with an embodiment of the present subject matter. The method 300 for detecting a fault in traffic signal using a system 102 may be described in the general context of device executable instructions. Generally, device executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like, that perform particular functions or implement particular abstract data types. The method 300 for detecting a fault in traffic signal using a system 102 may also be practiced in a distributed computing environment where functions are performed by remote processing systems that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage systems.
[0045] The order in which the method 300 for detecting a fault in traffic signal using a system 102 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 for detecting a fault in traffic signal using a system 102 may be considered to be implemented in the above-described system 102.
[0046] At block 302, a video stream associated with a traffic signal may be obtained. In one embodiment, the obtaining module 212 may obtain the video stream. Further, the obtaining module 212 may store the video stream in the system data 226.
[0047] At block 304, data packets may be generated based on an image processing methodology and the video stream. In one embodiment, the generation module 214. Further, the generation module 214 may generate data packets and store the data packets in the system data 226.
[0048] At block 306, an actual traffic signal model may be generated based on the data packets. In one embodiment, the generation module 214 may generate an actual traffic signal model and store the actual traffic signal model in system data 226.
[0049] At block 306, a fault in the traffic signal may be detected based on a comparison of the actual traffic signal model with a predefined traffic signal model. In one example, the predefined traffic signal model comprises a behavior model and a time model, and the predefined traffic signal model is indicative a correct functioning of the traffic signal. In one embodiment, the detection module 216 may detect a fault in the traffic signal and store the fault in the system data 226.
[0050] Although implementations for methods and systems for detecting a fault in traffic signal have been described in language specific to features, system and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods for detecting a fault in traffic signal described. Rather, the specific features and methods are disclosed as examples of implementations for detecting a fault in traffic signal.

Claims:
1. A method for detecting a fault in traffic signal, wherein the method comprising:
obtaining, by a processor, a video stream associated with a traffic signal, wherein the video stream is capture by a camera, wherein the traffic signal comprises one or more traffic light;
generating, by the processor, data packets based on an image processing methodology and the video stream, wherein the data packets comprise at least a unique identifier and a current state of the traffic signal;
generating, by the processor, an actual traffic signal model based on the data packets; and
detecting, by the processor, a fault in the traffic signal, based on a comparison of the actual traffic signal model with a predefined traffic signal model, wherein the predefined traffic signal model comprises a behavior model and a time model, and wherein the predefined traffic signal model is indicative a correct functioning of the traffic signal.

2. The method of claim 1, wherein the data packet is generated at a predefined time interval.

3. The method of claim 1, further comprises generating, by the processor, alert based on the fault.

4. The method of claim 1 further comprises
determining, by the processor, a type of fault based on the comparison; and
generating, by the processor, a notification based on the type of the fault, wherein the notification comprises data associated with a rectification of the fault.

5. A system for detecting a fault in traffic signal, wherein the system comprising:
a memory; and
a processor coupled to the memory, wherein the processor is configured to execute program instructions stored in the memory for:
obtaining a video stream associated with a traffic signal, wherein the video stream is capture by a camera, wherein the traffic signal comprises one or more traffic light;
generating data packets based on an image processing methodology and the video stream, wherein the data packets comprise at least a unique identifier and a current state of the traffic signal;
generating an actual traffic signal model based on the data packets; and
detecting a fault in the traffic signal based on a comparison of the actual traffic signal model with a predefined traffic signal model, wherein the predefined traffic signal model comprises a behavior model and a time model, and wherein the predefined traffic signal model is indicative a correct functioning of the traffic signal.

6. The system of claim 5 wherein the data packet is generated at a predefined time interval.

7. The system of claim 5 further comprises generating alert based on the fault.

8. The system of claim 5 further comprises
determining a type of fault based on the comparison; and
generating a notification based on the type of the fault, wherein the notification comprises data associated with a rectification of the fault.