Determining the Necessary Frame Rate of Video Data for Object Tracking under Accuracy Constraints

Network cameras, a type of surveillance cameras, generate real-time, versatile, and high quality video content that can be used for applications such as public safety and surveillance. Analyzing high frame rate video streams im- poses heavy computing needs and significant loads to the network. High frame rates may not be essential for meeting the accuracy requirements of the analyses. For example, high frame rates may not be required to track cars inside a garage compared with cars on a highway. In this paper, we study object tracking and propose a method to automatically determine the necessary frame rate for videos in network cameras for object tracking and adapt to run- time conditions. We demonstrate that the frame rates can be reduced up to 80% based on accuracy constraints.