Video Object Detection Method Using Single-Frame Detection and Motion Vector Tracking

Abstract

Video traffic on the Internet has been increasing rapidly and accounts for a large percentage of the total traffic. To process the increasing number of videos, edge computing is preferable for load balancing and bandwidth reduction. However, edge areas have less computational resources than cloud areas, and high-performance GPUs for processing videos at high speed are not always present. Therefore, a memory-saving and high-throughput video analysis method is necessary for analyzing videos in edge areas. In this paper, a video object detection method using single-frame detection and motion vector tracking is proposed. This method is classified as a pixel and compressed domain analysis method and is realized by compensating motion using the motion vectors that already exist in the compressed domain. This method is divided into two processes: CNN-based object detection and motion vector-based object detection. In addition, a network-transparent platform for video reconstruction in edge areas is constructed. The network-transparent service can be installed without modifying the existing end-device network settings, network configuration, and routing. The platform enables video object detection services to be added on without modification of these settings.