JOINT DETECTION, RE-IDENTIFICATION, AND LSTM IN MULTI-OBJECT TRACKING

Using Convolutional Neural Networks (CNN) in object tracking typically utilizes spatial features, while ignores the temporal correlation of frames in the whole film, causing that it is easy to lose the target when it is occluded by other objects. To cope with the problem, a robust system combining CNN and long short-term memory (LSTM) is proposed for multi-object tracking. The system consists of three modules: object detection, data association, and LSTM tracking. With the proposed approach, the tracking accuracy can be greatly improved especially when the tracking targets suffer from occlusion. Experimental results showed that the proposed system exhibits outstanding tracking accuracy and stability.