Multi-object tracking using online metric learning with long short-term memory

Abstract

A multi-object tracking (MOT) framework uses a dual Long Short-Term Memory (LSTM) network (Siamese) for MOT. The dual LSTM network learns metrics along with an online updating scheme for data association. The dual LSTM network fuses relevant features of trajectories to interpret both temporal and spatial components non-linearly and concurrently outputs a similarity score. An LSTM model can be initialized for each trajectory and the metric updated in an online fashion during the tracking phase. An efficient and feasible visual tracking approach using Optical Flow and affine transformations can generate robust tracklets for initialization. Thus, the MOT framework can achieve increased tracking accuracy. Further, the MOT framework has improved performance and can be flexible utilized in arbitrary scenarios.