Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance Systems

Abstract

In many distributed wireless surveillance applications, compressed videos are used for performing automatic video analysis tasks. The accuracy of object detection, which is essential for various video analysis tasks, can be reduced due to video quality degradation caused by lossy compression. This article introduces a video encoding framework with the objective of boosting the accuracy of object detection for wireless surveillance applications. The proposed video encoding framework is based on systematic investigation of the effects of lossy compression on object detection. It has been found that current standardized video encoding schemes cause temporal domain fluctuation for encoded blocks in stable background areas and spatial texture degradation for encoded blocks in dynamic foreground areas of a raw video, both of which degrade the accuracy of object detection. Two measures, the sum-of-absolute frame difference (SFD) and the degradation of texture in 2D transform domain (TXD), are introduced to depict the temporal domain fluctuation and the spatial texture degradation in an encoded video, respectively. The proposed encoding framework is designed to suppress unnecessary temporal fluctuation in stable background areas and preserve spatial texture in dynamic foreground areas based on the two measures, and it introduces new mode decision strategies for both intra- and interframes to improve the accuracy of object detection while maintaining an acceptable rate distortion performance. Experimental results show that, compared with traditional encoding schemes, the proposed scheme improves the performance of object detection and results in lower bit rates and significantly reduced complexity with comparable quality in terms of PSNR and SSIM.