Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

Content-aware H.264 encoding for traffic video tracking applications

The compression of video can reduce the accuracy of tracking algorithms, which is problematic for centralized applications that rely on remotely captured and compressed video for input. We show the effects of high compression on the features commonly used in real-time video object tracking. We propose a computationally efficient Region of Interest (ROI) extraction method, which is used during standard-compliant H.264 encoding to concentrate bitrate on regions in video most likely to contain objects of tracking interest (vehicles). This algorithm is shown to significantly increase tracking accuracy, which is measured by employing a commonly used automatic tracker