DEEP FEATURE BASED END-TO-END TRANSPORTATION NETWORK FOR MULTI-TARGET TRACKING

We propose an End-to-End Transportation Network (EETN) for multi-target tracking. In the EETN, we model the optimal set of trajectories through min-cost flow problem by exploring deep features to generate a graph. The transition cost among the nodes is found through statistical similarity metric. We consider dynamic programming to solve the optimization problem. For experimental evaluation, we compare our proposed EETN method with two state-of-the-art methods using four benchmark datasets. The quantitative analysis shows promising results of our EETN against state-of-the-art methods on precision/recall and F-score.