GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking

• URL: http://arxiv.org/abs/2012.14314v1
• Date: Mon, 28 Dec 2020 15:52:24 GMT
• Title: GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking
• Authors: Zhen Li, Sunzeng Cai, Xiaoyi Wang, Zhe Liu and Nian Xue
• Abstract summary: 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. We propose 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.
• Score: 8.559199703957393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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