Focus! Provisioning Attention-aware Detection for Real-time On-device Video Analytics

The detection of objects via neural networks plays a key role in various video analytics, but consumes huge resources. Due to the limited on-device computing capability, such real-time detections should be precisely used for the objects that need the most attention. Unfortunately, as the target objects keep moving, existing systems fail to conduct adaptive detections over multiple regions in a video, and existing tracking mechanisms are easily affected by background contents. Therefore, we propose to design attention-aware on-device detection for analytics, to increase the quality of detections for those targets. However, the uncertainty of detections, the attention shift over regions, and the provisioning of on-device resources essentially hamper us from efficient analytics. We formulate such a scenario as a non-linear integer program in long-term scope, to maximize the detection accuracy. Afterwards, we design an online mechanism to orchestrate the detection model for each region in the video to cope with the moves of the targets, via a queue-based adaptation and the randomized rounding. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement for the video analytics are ensured. The testbed experiments confirm the superiority of our approach over multiple state-of-the-art algorithms.