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

Tracking-optimal pre- and post-processing for H.264 compression in traffic video surveillance applications

The compression of video can reduce the accuracy of automated tracking algorithms. This is problematic for centralized applications such as transportation surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. In typical systems, the majority of communications bandwidth is spent on representing events such as capture noise or local changes to lighting. We propose a pre- and post-processing algorithm that identifies and removes such events of low tracking interest, significantly reducing the bitrate required to transmit remotely captured video while maintaining comparable tracking accuracy. Using the H.264/AVC video coding standard and a commonly used state-of-the-art tracker we show that our algorithm allows for up to 90 bitrate savings while maintaining comparable tracking accuracy