Multiple Object Tracking with Motion and Appearance Cues
This work addresses real-world challenges in multiple object tracking for applications like surveillance, but it is incremental as it builds on the tracking-by-detection scheme.
The paper tackles camera motion and missing detections in multiple object tracking by using optical flow and an auxiliary tracker, improving performance significantly on the VisDrone-MOT dataset while maintaining high efficiency.
Due to better video quality and higher frame rate, the performance of multiple object tracking issues has been greatly improved in recent years. However, in real application scenarios, camera motion and noisy per frame detection results degrade the performance of trackers significantly. High-speed and high-quality multiple object trackers are still in urgent demand. In this paper, we propose a new multiple object tracker following the popular tracking-by-detection scheme. We tackle the camera motion problem with an optical flow network and utilize an auxiliary tracker to deal with the missing detection problem. Besides, we use both the appearance and motion information to improve the matching quality. The experimental results on the VisDrone-MOT dataset show that our approach can improve the performance of multiple object tracking significantly while achieving a high efficiency.