Poster: Stochastic Scheduling on Object-sparse Video Data

One major research problem regarding mobile sensing systems is how to accelerate the processing speed which is bottle-necked by the deep-learning-based object detection. The focus of this paper is directed towards a typical mobile sensing scenario wherein sequences of frames containing interested objects are sparsely dispersed throughout the video stream. In light of this, we propose a stochastic scheduling algorithm named JumpQ. In the case of consecutive negative detections, JumpQ reduces the probability of detection, while in the case of positive detections, JumpQ promptly returns to frame-by-frame detection and retraces the buffered frames to detect the objects. Our experiment reports that the JumpQ algorithm accelerates processing speed by over 100%, all while incurring a negligible impact on sensing accuracy.