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Quality-adaptive deep learning for pedestrian detection

Pedestrian detection is a fundamental task for many applications including autonomous vehicles and surveillance systems. In a mobile or networked environment bandwidth is limited and adaptive datarate streaming is used. Video compression can introduce significant quality degradation that impacts the accuracy of video analytics. In this paper, we examine the problem of a changing video data-rate and examine how it affects the performance of video analytics, in particular pedestrian detection, using a two-stage quality-adaptive convolutional neural network system. Our experimental results demonstrate that when adaptive data-rate streaming is used, our proposed quality-adaptive approach reduces the miss rate by 20% compared to the baseline detector.