  Multiple Object Tracking (MOT) has been a useful yet challenging task in many
real-world applications such as video surveillance, intelligent retail, and
smart city. The challenge is how to model long-term temporal dependencies in an
efficient manner. Some recent works employ Recurrent Neural Networks (RNN) to
obtain good performance, which, however, requires a large amount of training
data. In this paper, we proposed a novel tracking method that integrates the
auto-tuning Kalman method for prediction and the Gated Recurrent Unit (GRU) and
achieves a near-optimum with a small amount of training data. Experimental
results show that our new algorithm can achieve competitive performance on the
challenging MOT benchmark, and faster and more robust than the state-of-the-art
RNN-based online MOT algorithms.
