论文标题
GAKP：GRU协会和Kalman的多个对象跟踪预测
GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking
论文作者
论文摘要
在视频监视，智能零售和智能城市等许多现实世界应用中，多个对象跟踪（MOT）是一项有用但具有挑战性的任务。挑战是如何以有效的方式对长期时间依赖性建模。最近的一些作品采用了经常性的神经网络（RNN）来获得良好的性能，但是，这需要大量的培训数据。在本文中，我们提出了一种新颖的跟踪方法，该方法将自动调整Kalman预测方法和封闭式复发单元（GRU）集成在一起，并与少量的训练数据达到了近乎最佳距离。实验结果表明，我们的新算法可以在具有挑战性的MOT基准上实现竞争性能，并且比基于最新的RNN在线MOT算法更快，更强大。
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.
