SOT for MOT
This work addresses tracking accuracy in computer vision for applications like surveillance and autonomous driving, but it appears incremental as it builds on existing tracking-by-detection frameworks.
The paper tackled the multiple object tracking problem by combining single object tracking algorithms with multiple object tracking to reduce false negatives, and used a deep learning appearance model for efficient and accurate data association, achieving state-of-the-art performance on the MOT16 benchmark.
In this paper we present a robust tracker to solve the multiple object tracking (MOT) problem, under the framework of tracking-by-detection. As the first contribution, we innovatively combine single object tracking (SOT) algorithms with multiple object tracking algorithms, and our results show that SOT is a general way to strongly reduce the number of false negatives, regardless of the quality of detection. Another contribution is that we show with a deep learning based appearance model, it is easy to associate detections of the same object efficiently and also with high accuracy. This appearance model plays an important role in our MOT algorithm to correctly associate detections into long trajectories, and also in our SOT algorithm to discover new detections mistakenly missed by the detector. The deep neural network based model ensures the robustness of our tracking algorithm, which can perform data association in a wide variety of scenes. We ran comprehensive experiments on a large-scale and challenging dataset, the MOT16 benchmark, and results showed that our tracker achieved state-of-the-art performance based on both public and private detections.