CCTV - Video Analytics for Traffic Management

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1 CCTV - Video Analytics for Traffic Management Index Purpose Description Relevance for Large Scale Events Technologies Impacts Integration potential Implementation Best Cases and Examples 1 of 12

2 Purpose Intelligent image detection systems are part of a centralized approach to modern traffic management. This has arisen from the need for more cost effective and efficient monitoring of traffic. In turn, this has increased the scope for automatics analysis of urban traffic activity from CCTV in recent years. This increase can be contributed in part to the additional numbers of cameras and other sensors, enhanced infrastructure and consequent accessibility of data. Also the advancement of analytical techniques to process the video (and other) data together with increased computing power has enabled new applications. Video analytics is defined as computer vision based surveillance algorithms and systems to extract contextual information from video. The main concept is to aid human operators in observing video data. This can allow online and post-event detection of events of interest, which is useful for traffic management due to additional data available. The current main bottleneck of surveillance is the limitation of human resources for observing hundreds of cameras. Automatic pre- processing allows efficient guidance for the operators to pick cameras to view and accumulate statistics, with the aim to improve traffic flow. Video cameras have been deployed for a long time for traffic and other monitoring purposes, because they provide a rich information source for human understanding. Video analytics may now provide added value to those cameras by automatically extracting relevant information and easing the bottleneck of operators viewing all cameras. top Description At its highest level Video Analytics provides the capability of automatically analysing video to detect and determine events. This allows a wealth of data to be collected from what is often a very cheap and well understood sensor. This analysis is carried out in many and varied ways but in essence the following process can be considered generic to most systems Figure 1 - Video Analytics Basics This shows that the system first removes the background parts of the image (background subtraction); leaving only the foreground, containing the objects we wish to analyze. This foreground is then split into different objects. These objects can then be given attributes such as size, position, shape color and texture. These attributes can then be used to track the objects through the scene providing some contextual data. In order to make sense of this wealth of data and turn it into information logical rules can be created to trigger events based on certain scenarios such as Send an alert when a person (shape\size) enters the works area (position) and stays there for 2 minutes (time) 2 of 12

3 As it can be seen this approach is very flexible and can account for a vast multitude of scenarios in which Video Analytics can be used (and indeed this has led to a widespread adoption of VA in many sectors). However, as the rules used to trigger events are often very basic to achieve reliable results the resulting logical rules can often become complex, to remove unwanted false alarms. Herein lies the stumbling block for VA, it often requires well trained operatives to set up the rules. Some of the challenges faced when deploying video analytics technology are intrinsic to the way the technology works. Due to the reliance on the background subtraction process to determine the objects VA systems can sometimes lose objects (stationary vehicles) or falsely detect others (trees, rubbish etc...). This can be complicated by environmental issues such as rain on the camera lens or windy conditions vibrating the camera \ pole making it difficult to distinguish the background. The more developed VA systems have methods of minimizing these effects and can make use of complex logical operations to remove unwanted alerts. These very challenges are why it is necessary to test and evaluate systems to ensure high performance and accuracy. Detection: description of detection mechanisms. Video Analytics Classification Foreground estimation and segmentation is the first stage of many visual surveillance systems. The foreground regions are marked for processing in the subsequent steps. The foreground is defined as every object, which is not fixed furniture of a scene where fixed could normally mean months or years. This definition conforms to human understanding, but it is difficult to implement algorithmically. There are two main different approaches to estimate the foreground, which both use strong assumptions to comply with the above definition. Firstly, a background model of some kind can be used to accumulate information about the scene background of a video sequence. The model is then compared to the current frame to identify differences (or motion ), provided that the camera is stationary. This concept lends itself well for computer implementation, but leads to problems with slow moving traffic. Any car should be considered foreground, but stationary objects are missed due to the lack of motion. Possibly the simplest method for foreground segmentation is frame differencing. A pixel by pixel difference map is computed between two consecutive frames. This difference is thresholded and used as foreground mask. This algorithm is very fast, 3 of 12

4 however, it cannot cope with noise, abrupt illumination changes or periodic movements in the background like trees. Classical visual surveillance approaches of background modeling and tracking have been successfully applied for highway surveillance. There are attempts to overcome the problem of occlusion and shadows for that type of scene. Urban environments are more challenging due to denser traffic, variable orientation of vehicles at intersections and lower camera position. More advanced approaches have been suggested including 3D models, shadow prediction, appearance models etc. Algorithms developed for the generic object recognition domain have been applied and show promising results in the urban traffic domain. From an application perspective, the main technical challenge is the diversity of camera views and operating conditions in traffic surveillance. In addition, a large variety of observation objectives like vehicle counting, classification, incident detection or traffic rule enforcement can be useful. This has generated a large and diverse body of work, where it is difficult to perform direct comparison between proposed algorithms. Example of danger Video Analytics Integrated with Traffic Control Centre top Relevance for Large Scale Events Video Analytics are extremely useful tools for large events as they are able to interpret movement of people and vehicles in real time and suggest actions to an operator, often when an operator would not otherwise have been aware of an issue. Potential uses of Video Analytics during large events include the ability to: Interpret conditions without operator overhead, thereby freeing up resources for other activities. Alerting operator to suspected adverse conditions 4 of 12

5 Providing timely snapshot of conditions and ability to link to graphical user interface with CCTV/Video feeds Feed information to database management system to correlate with other data feeds Ability to work 24/7 during all weather types (subject to stable configuration and suitable operating conditions) top Technologies Video Analytics use a CCTV camera, either static or PTZ (Pan Tilt Zoom), connected to a CCTV matrix that is then processed via a video server. This server connects to the users through an appropriate firewall. In addition, smart cameras can be used that perform the analysis on site and sends just the alerts to the users via a suitable secure channel. 5 of 12

6 The management of Video Analytics requires an operator interface, normally which is a web-based system in order to receive and share information on the alerts. By courtesy of Ipsotek 6 of 12

7 The software can be configured to display a multitude of parameters, including: Detection Event Spatial Location Type of Incident What camera feed is being used Input field for operator Input on validity of alert or not (to track the accuracy of the alerts being generated) top Impacts The interest in Image Detection systems has arisen from the need for more cost effective and efficient monitoring of traffic. It is not feasible to monitor every one of the CCTV cameras available. This technology will allow Traffic Controllers to be alerted to cameras which can provide important information. The London Traffic Control Centre highlighted this fact: There is a limitation to how many staff can be used to monitor cameras, IRID (Image Recognition Incident Detection) provides another set of eyes, although unable to perform any corrective action, it s a benefit to the team in being alerted to an issue, missed while monitoring other parts of the TLRN. Its benefit is also apparent when the team are dealing with severe incidents, whilst unable to monitor cameras in that area due to other issues they are dealing with, IRID will alert them to new issues possibly as a result of the original one or a new and separate issue. A PTZ CCTV camera on street may only be watched for 30 minutes in a day. It can easily be seen that this does not represent best value as a return on investment. Video Analytics will be directly implementing a system to monitor CCTV cameras whilst they are not being watched, thus increasing their monitored time. top Integration potential The effectiveness of Video Analytics as part of the traffic management strategy is greatly increased when they are integrated with other ITS systems such as, for example: In urban areas: Traffic Management System Parking Management System Overall supervisor for urban transport (high level management platform) top Implementation Video Analytics can be configured to detect a number of different events such as: 7 of 12

8 Congestion Illegal turns Parked vehicles Yellow box stopped vehicles Approaching vehicles Traffic counts Stopped traffic Unusual activity Etc In order to achieve accurate alert signals, a high level of configuration is required, identifying the areas of interests and linking it to the type of alert that is expected In addition, a clear view of the road requires the camera to be installed at a height of over 4 meters and with the area of interest within the near zone of view of the cctv feed. Typical Detection Zones outlined with associated alerts Configuration options within video analytics are multiple, with parameters including thresholds, time of day, smoke detection, camera movement, reflection levels, lighting options etc all to be considered. The costs of the Video Analytics (VA) system itself depends on the type of camera used, the analytics algorithm employed, the data feeds and the method of integration with a database engine (if required). In addition, SMART cameras, which perform the processing locally or a suitable alterative to regular CCTV cameras linked to a centralized data engine. It was estimated in 2010 that in UK that the purchase and installation for the purchase of a VA system for 12 channels + 6 SMART Cameras is ~ 60k. 8 of 12

9 top Examples Red Light Cameras Improve Safety on the Rochester Streets The Rochester Police Department is deploying the latest technology to keep motorists and pedestrians safe. As part of Rochester s Red Light Photographic Enforcement Program, cameras are being installed at certain intersections to help enforce the vehicle and traffic law. The intersection locations were selected based upon accident data and video surveys conducted by Redflex Inc., the company that is managing the program for the City. The program began in October How Red Light Traffic Safety Cameras Work The cameras will capture still and video images of vehicles in the act of a red-light violation, which will initiate the procedure to deliver a Notice of Liability to the registered owner of the vehicle. The violation is a civil matter and will not be reported to insurance companies or generate points on a driver s license. Evidence captured by the Red Light Cameras is reviewed three times and approved by the Rochester Police Department before a Notice of Liability is delivered in the mail to the registered owner of the vehicle. The cameras operate 24 hours a day and capture still photographs and video of every vehicle that runs a red light at the intersection. Cameras photograph only the vehicle and license plate of vehicles running the red lights. No images of the driver or passengers are captured. 9 of 12

10 Vehicle owners are responsible for violations by operators of their vehicle. Vehicle owners will have an opportunity to appeal the Notice or pay the fine. The civil violation carries an initial $50 fine. If the initial fine is not paid within 30 days, an additional penalty of $25 will be assessed. The registered owner who receives the notice has the following options to resolve the violation: Pay the fine either by mail (no cash please), phone using a credit card, or in person at Parking Violations Bureau at 42 South Ave. Pay online at Contest the violation in the Parking Violations Bureau. For information call (585) or (585) Violators may view a 12-second video clip provided by the vendor, Redflex, Inc. at The City of Rochester contracts with RedFlex to provide the Red Light Camera Enforcement Program. Redflex, Inc is installing and maintaining all equipment related to this program and process. If the fine is not paid or contested within 30 days, a Notice of Impending Default Judgment will be sent informing the vehicle owner that an additional $25 has been assigned to the originial $50 fine. If the City does not a receive a response, a Notice of Judgment will be delivered and the case will be sent to the City s Collections Agent, EOS CCA, for collection. The effectiveness of speed cameras: The English experience Safety (or speed) cameras provide a valuable and cost-effective method of preventing, detecting and enforcing speed and traffic light offences. They encourage changed driver behaviour and are also proven to make a significant contribution to improving road safety for all road users. Background & Objectives In the UK, on average, nine people are killed and 85 injured each day. One measure to reduce speeding and make roads safer are speeds cameras (red-light or safety cameras). Implementation Safety Cameras are deployed and operated by local partnerships usually made up of local authorities, Police and HM Courts Service Conclusions A Four-year evaluation of 3,800 safety camera sites in 34 local authority partnership areas found that: Vehicle speeds - Surveys showed that vehicle speeds at speed camera stes had dropped by around 6% following the introduction of cameras Casualties and deaths statistics- Overall there was a 22% reduction in personal injury collisions at sites where cameras had been introduced, 42% fewer people were killed or seriously injured 10 of 12

11 Public support- Public attitude surveys at both local and national level supporteed the use of speed cameras for targetted enforcement Cost-benefit analysis- It was estimated that in the fourth year, the benefits to society from the avoided injuries were in excess of 258 million (381 million Euro) compared to the enforcement costs of around 96 million (141 million Euro) TRANSPORT FOR LONDON Transport for London recently implemented a trial of Video Analytics under the title of IRID Image Recognition for Incident Detection (IRID). 17 IRID locations have ANPR coverage and hence journey time comparisons before and after IRID implementation could be conducted. Out of the chosen locations, 11 (65%) showed journey time reductions, whilst the remaining 6 (35%) showed increases in journey time. IRID location (Lower Thames Street / London Bridge) showed the greatest reduction in journey times (23.1%), whilst IRID location (A501 City Road westbound from Sheperdess Walk) had the highest increase in journey (20.1%). The least amount of change was detected at IRID locations (A40 Hillingdon Circus) and (West Hill) with 1.0% and 0.8% respectively. 11 of 12

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