On Exploiting Tile Partitioning to Reduce Bitrate and Processing Time in VVC Surveillance Streams with Object Detection

One of the main targets in video surveillance systems is to detect and possibly identify objects within monitoring range. This entails analyzing the video stream, by applying object detection techniques on one or more frames. Regardless of the output, the stream is usually archived for future use. Real-time requirements, network bandwidth, and storage constraints play a significant role to total performance. As video resolution increases, so does the video stream size. To harness such an increase, newer video compression standards offer sophisticated coding tools that aim at reducing video size, with minimal quality loss. However, as the achievable compression ratio increases, so does the computational complexity. In this paper, we propose a methodology to reduce both bitrate and processing time of video surveillance streams whereby object detection is performed. The method takes advantage of tile partitioning, with the aim of (i) reducing the scope and the invocation frequency of the object detection module, (ii) encoding different blocks of a frame at different quality levels, depending on whether objects exist or not, and (iii) encoding and transmitting only tiles containing objects. Experimental results using the UA-DETRAC dataset and the VVenC encoder demonstrate that exploiting tile partitioning in the manner proposed in the paper results in reducing bitrate and processing time at the expense of only tiny losses in accuracy.