COST-EFFECTIVE VIDEO ANALYTICS IN SMART CITIES

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1 COST-EFFECTIVE VIDEO ANALYTICS IN SMART CITIES WHITE PAPER Abstract As technology abounds, the Smart City trend is taking shape in many countries. Its implementation comes with some challenges, though - traffic congestion, public safety, and the integration of various infrastructures through analyzed data. When we say smart, the first thing that comes to mind is an intelligent system made possible through the analysis of huge chunks of data. Video would be the most effective data form for analysis and for making quick or real time response systems in Smart Cities, whether it is related to safety, security, crowd management, traffic management, smart retail processes or healthcare. Video analytics is a powerful tool that has the potential to convert unstructured video data into structured useful data which can be analyzed, searched, and managed to create a real-time intelligent response system. The introduction of intelligent video analytics in Smart City applications also comes with some challenges, such as scalability, reliability, speed, and cost-effectiveness. Market Trends and Challenges The global video analytics market has changed dramatically over the years. The trend has moved from standalone to networked solutions, from safety and security to other uses in verticals like retail, healthcare, traffic management, education, sports and in numerous other fields. As more sophisticated situations arise out of the Smart Cityconcept, and as the need for intelligent security systems arise due to rising criminal/terrorist activity, cost-effective, efficient & intelligent video analytics appear to be the need of the hour. In 2025, it is expected we will have around 26 global Smart Cities and around 50 percent of these will be located in North America and Europe [2]. Analysts forecast that the video analytics market will grow at a CGR of percent over the period of and grow to $867.8 million by Latin America, and Middle East and Africa regions are emerging markets, whereas Europe, Asia-Pacific, and North America regions are considered high growth markets [1]. An intelligent Video analytics is predominantly implemented in two different configurations server-based analytics, and edge-based analytics. With video analytics moving to the cloud and for online video analytics, the major challenge would be to reduce the load on a carrier network. If we compress videos, their quality is compromised due to both the compression and the transmission of the data on the network. In addition, uncompressing the video at the server adds an extra processor demand on the server, which decreases its efficiency for analytics. Another challenge that arises in server-based analytics is the increase in processing demand on the server, particularly as all video analytics processing is processor intensive and takes place on the server. Therefore, when adding morecameras, either the server needs to be upgraded or system restructuring needs to be done for adding extra servers. With Smart Cities taking shape, intelligent security and surveillance, smart traffic management and smart retail is becoming extremely important. Advanced technology for crime management by installing video surveillance

2 WHITE PAPER/1 systems in Smart Cities will cost around 10 percent of the overall cost to set up the city [3]. Currently, network and camera infrastructure exists for video surveillance. Reusing the existing resources would help reduce the investment and make video analytics solutions more cost-effective. So, edge-basedanalytics, which would require replacing existing cameras with high-end smart cameras with analytical features, would not prove to bebeneficial in this regard. In addition, the limitation in processing power in these devices compromises the efficiency and performance of video analytics. Video analytics is useful across all verticals. In security and surveillance, it can be used for object detection, asset security, loitering detection, overcrowding identification, emergency response scenarios; yet in all these cases, the challenge of false alarms creep in. Moreover, algorithm accuracy in such analytics and in more sophisticated analytics such as face recognition, license plate recognition, and vehicle direction and count for traffic management, has always been a concern for service providers providing video analytics services. All these challenges - of reducing carrier network load, hardware dependency for video processing, limited parallel processing on servers, reusing existing camera and network infrastructure, accuracy of algorithms in various environments, and false alarms, have been the key inhibitors of the video analytics market. The Solution Edge Devices with basic Analytics Any Network Advanced Analytics Configurable Alerting Any Usecase Cost Effective Customizable Algorithms Hardware Independence Intituve Configuration Missing Objects Left behind Objects Smart Cities Metadata New Objects Object Movement Smart Retail Comprehensive Realtime & Offline Video Analytics Object Count Transportation Distributed Architecture / Leveraging the built-in features of the Camera System To meet the challenges of server-based analytics we can have distributed architecture for video analytics. In this system, the workload of the server would be distributed among the edge devices and the server. For this approach, edge devices like cameras, smart phones, smart tablets, etc. will have some analytics capability like motion, object, and color detection, distinguishing objects from environmental noise, and detecting moving or stationary objects which, in turn, would create metadata that would be transferred over the network, separately from the digital video stream, to the server for further analysis. The server would be configurable to set the conditions in the recorder for the alarm system. Server processing would include receiving and storing metadata from edge devices and extracting the objects which match the conditions already set.it would also be responsible for reporting the result and creating custom alert systems which have been pre-configured. Once the alert has been raised, the user can be given a choice to stream or store only the events which are cause for concern. All this would help reduce the load on the carrier network and help in customizing storage. come of the existing cameras being used for video analytics already have basic analytic capabilities like motion detection, lighting detection and more, while some cameras may not. Reusing such existing infrastructure later would require severs to do most of the analytics. In such scenarios, we need to design processing on servers in such a way that adding more cameras should not pose more challenges like server upgradation or restructuring. For this to be achieved, existing video analytics frameworks need to bere-architectured in such a way that server processing is distributed to reduce the load on any particular server. Also, breaking down the processing and analytics steps in a modular manner will help to scale them up independently, based on need. This would help in cost reduction because the same hardware would be capable of handling more cameras and each component of the framework would run on any commodity hardware without the requirement of high-end servers for small scale operations.

3 WHITE PAPER/2 Configurable Analytics System Video analytics algorithms depend a lot on the environment for analysis, on face recognition factors like lighting, background, and face orientation due to changing emotions, which often leads to inaccuracy. These inaccuracies and false alarms can be dealt with if the user has the option to configure some parameters depending on the situation for which the analytics has to be performed. And based on these configurations, customized algorithms or best suited algorithms can be applied. This can certainly help us enhance the accuracy of the algorithms and reduce the false alarms. Case Study Almost 60 percent of the world s population is expected to live in urban areas by 2025, increasing the growth of Smart Cities, which would entail a need for innovative and efficient technologies to foster intelligent systems. Video data and analysis would play a major role in designing such systems. Existing city cameras can act as sensors for activity and the input from these cameras can be fed into customized algorithms for analysis. The most important aspect of Smart Cities would be intelligent security and surveillance. With video analytics, remote and unmanned monitoring is possible. For example, there would be no need for manual monitoring, or unattended object detection, or for securing valuable assets, or for illegal parking, or for intruder detection. All this can be efficiently and automatically done by using configurable video analytics which can help in the reduction of false alarms, and more importantly, in leveraging real time custom alerts so that proper and timely action can be taken. This can also be applied in many other cases such as loitering detection, crowd monitoring, people counting, vandalism, queue management, and more. Face recognition systems can help us reduce criminal activities byhelping us nab criminals faster. Intelligent traffic management would be another important aspect of Smart Cities. Vehicle counting in high traffic areas can help in rerouting to prevent traffic congestion. License plate recognition helps in reducing traffic rule violations. Wrong way, Illegal parking, speed zones, and vehicle tracking are some of the other applications of video analytics, which will help increating a smart traffic system with real time traffic assistance Common Issues A major challenge in the solution would be concerning the accuracy of the algorithms and the reduction of false alarms. As in unattended object detection, the configuration parameters should be carefully chosen based on the scenario it has to be used in. For example,configuration parameters would vary when cameras are placed in crowded areas, as chances of false alarms increase in such scenarios. For face recognition, the function of matching faces from a database should be optimized for better analytical performance since thedatabase contains thousands of images. As various modules of frequently used analytics frameworks are closely coupled, it is indeed challenging to re-architect these frameworks to make them hardware independent and enable them to run on any commodity hardware. Conclusion To achieve more intelligent systems, large amounts of data need to be collected from across the city, at each instant, and then analyzed to extract useful information to make decisions and create anintelligent response system. Smart City solution providers will face challenges in increasing network load due to the huge amounts of video data flowing through their networks. Then comes theintegration of video analytics with the city s existing infrastructure and the algorithms, depending onthe scenarios in which they are used. Video analytics has been in the market for a long time but must scale up with the changing trends and sophisticated requirements. For cost-effective analytics, distributed architecture with user control seems to be a good solution. The architecture must leverage the built in features of existing cameras, thereby reducing the need for setting up infrastructure from scratch. This video analytics platformidentifies the most accurate algorithms/components depending on the configuration, and is able to report the results and create custom alerts in a pre-configured way to attain better accuracy. For efficiency and independence from hardware, video analytics processing can be distributed across commodity hardware, which will help reduce cost by avoiding the need for

4 WHITE PAPER/3 high-end servers. In Smart Cities with varying applications of video analytics solutions in fields such as security systems, utilities operators, and emergency response systems, it gives users a simple way to pick the feed they would like, instrument the analysis they want, and report the way they require in a simple-configurable manner. Author Anurag Choubey; Technical Lead Reference [1] [2] [3]

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