DeepAF: Transformer-Based Deep Data Association and Track Filtering Network for Multi-Target Tracking in Clutter

Based on the transformer model, a deep data association and track filtering network (DeepAF) was constructed in this paper to achieve the function of data association and end-to-end track filtering. Combined with the existing track initiation methods, DeepAF can be used to track multiple targets in clutter environments. Experimental results show that DeepAF can stably and effectively track targets moving in different models such as constant velocity, constant acceleration, and constant turn rate. Compared with the probability hypothesis density filter and the probabilistic data association method, which were set with different state transition matrices manually to match with the actual target motion models, DeepAF has similar estimation accuracy in respect of target velocity and better estimation accuracy in respect of target position with less time consumption. For position estimation, compared with PHD, DeepAF can reduce the estimation error by 49.978, 49.263, and 2.706 m in the CV, CA, and CT motion models. Compared with PDA, DeepAF can reduce the estimation error by 13.465, 23.98, and 4.716 m in CV, CA, and CT motion models. For time consumption, compared with PHD, DeepAF can reduce the time by 991.2, 982.3, and 979.5 s in CV, CA, and CT motion models. Compared with PDA, DeepAF can reduce the time by 61.6, 60.5, and 61.4 s in CV, CA, and CT motion models.