Optimizing Queries over Video via Lightweight Keypoint-based Object Detection

Recent advancements in convolutional neural networks based object detection have enabled analyzing the mounting video data with high accuracy. However, inference speed is a major drawback of these video analysis system because of the heavy object detectors. To address the computational and practicability challenges of video analysis, we propose FastQ, a system for efficient querying over video at scale. Given a target video, FastQ can automatically label the category and number of objects for each frame. We introduce a novel lightweight object detector named FDet to improve the efficiency of query system. First, a difference detector filters the frames whose difference is less than the threshold. Second, FDet is employed to efficiently label the remaining frames. To reduce inference time, FDet detects a center keypoint and a pair of corners from the feature map generated by a lightweight backbone to predict the bounding boxes. FDet completely avoid the complicated computation related to anchor boxes. Compared with state-of-the-art real-time detectors, FDet achieves superior performance with 29.1% AP on COCO benchmark at 25.3ms. Experiments show that FastQ achieves 150 times to 300 times speed-ups while maintaining more than 90% accuracy in video queries.