Quantization optimized H.264 encoding for traffic video tracking applications

The compression of video can reduce the accuracy of post-compression tracking algorithms. This is problematic for centralized applications such as traffic surveillance systems, where remotely captured and compressed video is transmitted to a central location for tracking. We propose a low complexity optimization framework that automatically identifies video features critical to tracking and concentrates bitrate on these features via quantization tables. Using the H.264 video coding standard and two commonly used state-of-the-art trackers we show that our algorithm allows for over 60% bitrate savings while maintaining comparable tracking accuracy.