The cloud-based analytics offers intelligent video services without the need for additional hardware or licensing fees. The data coming from different locations through a single web-based interface is analyzed at one place. Since analog cameras work with digital video recorder (DVR) storage technology, they cannot highlight any event. In case of a crime detection event, it might take a longer period of time to play or analyze the full video. However, an IP system with video analytics can highlight or flag events with functions such as motion detection, camera tampering, and a range of other events.

With the increasing adoption of video surveillance systems for various applications such as infrastructure security, business intelligence, and preserving law and order, the dependence on these surveillance systems has gradually increased. Their performance not only depends on efficiency but is also driven by promptness, accuracy, and embedded processors employed. These processors basically provide features such as securing intellectual property, lowering power dissipation, minimizing cost, and reducing the overall development time. Therefore, the developers are readily grabbing the opportunity of introducing such processors. The existing options for processors in the video surveillance systems are digital still camera (DSC) processors, application-specific integrated circuits (ASIC), digital signal processors (DSP), and field-programmable gate arrays (FPGA).

With the rising privacy concerns, the demand for bandwidth in order to set up a video surveillance system is one of the major concerns. The major challenge in providing cloud-based video services is the lack of available bandwidth. High bandwidth internet connection is required for the rapid transmission of data with a shorter response time, which may not be available at remote locations. Since real-time analysis and detection are not possible at such locations, they pose as a challenge against the deployment of video surveillance systems.

The rise in public security concerns is expected to create the need for the deployment of more surveillance systems. Further, governments of various countries are deploying video cameras on a large scale to create a stronger security system for their respective countries. With a greater number of cameras being installed globally, there arises an impending need for more people to monitor those cameras. It is extremely difficult and expensive to employ and manage a workforce big enough to monitor those many cameras. However, this issue can be solved by utilizing video content analytics (VCA), video surveillance as a service (VSaaS), and real-time analytics.

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Video analytics has the ability to reduce the huge amount of data available in the video, thus making it more controllable. The benefits of video analytics include reduced load on network and storage demand, easy and fast retrieval of stored video, and productive use of manpower, among others. Moreover, in case of any abnormal activity, the system raises an alarm to alert the concerned officials. Additionally, with the advantage of analyzing live as well as recorded data, the need for reviewing the recorded data by a human operator, which is a quite expensive resource, has been eliminated. These features, along with the numerous technological advancements, such as facial recognition and automatic license plate recognition, make the video surveillance cameras appropriate for application in different verticals, especially in the current scenario. Therefore, with the help of VSaaS, and video analytics, the time and monetary benefits can be availed by the installer. Bosch Security Systems and Honeywell are some of the major market players providing video analytics-based solutions. Below are some instances which highlight the demand for VCA and VSaaS.