Hybrid Online Visual Tracking of Non-rigid Objects

Visual object tracking has been a fundamental topic of machine vision in recent years. Most trackers can hardly top the performance and work in real time. This paper presents a tracking framework based on the SiamFC network, which can be taught online from the beginning of tracking and is real time. SiamFC network has a high tracking speed but cannot be trained online. This limitation made it unable to track the target for a long time. Hybrid-Siam can be trained online to distinguish target and background by switching traditional tracking and deep learning methods. Using the traditional tracking method and a target detector based on saliency detection has led to long-term tracking. Our method runs at more than 60 frame per second during test time and achieves state-of-the-art performance on tracking benchmarks, while robust results for long-term tracking. Hybrid-Siam improves SiamFC and achieves AUC score 81.7% on LaSOT, 72.3% on OTB100, and average overlap of 66.2% on GOT-10 k.