Online multi-target intelligent tracking using a deep long-short term memory network

Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise. Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario. In this paper, considering the model-free purpose, we present an online Multi-Target Intelligent Tracking (MTIT) algorithm based on a Deep Long-Short Term Memory (DLSTM) network for complex tracking requirements, named the MTIT-DLSTM algorithm. Firstly, to distinguish trajectories and concatenate the tracking task in a time sequence, we define a target tuple set that is the labeled Random Finite Set (RFS). Then, prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets, respectively. Further, the prediction block can learn the movement trend from the historical state sequence, while the update block can capture the noise characteristic from the historical measurement sequence. Finally, a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths. Experimental results manifest that, compared with the existing tracking algorithms, our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions, and be applied to linear and nonlinear multi-target tracking scenarios.