A saliency-based rate control for people detection in video

Most of latest-generation multimedia systems are equipped with increasingly-effective object detection algorithms (e.g., intelligent video surveillance systems, augmented reality applications, sharing platforms for multimedia data, etc.). Unfortunately, images and video are usually available in compressed formats, which makes object detection more difficult because of the additional distortion noise. In this paper we show that it is possible to mitigate this problem by introducing a rate allocation algorithm that preserves important details for object identification algorithms. We propose a saliency map that identifies crucial elements for detectors. Then, we map saliency values to the value of the quantization parameter to be used by the video coder. Experimental results on HEVC coder show that the proposed rate control algorithm improves the accuracy with respect to the standard strategy.