Tracking a human fast and reliably against occlusion and human-crossing

Abstract

Tracking a human using the computer vision techniques is essential in the automatic surveillance task. Not only its accuracy and speed but also how it deals with occlusion and human-crossing are the challenges for a reliable tracking framework. Among many, Kernelized Correlation Filter (KCF) has become a state-of-the-art tracker partly because of its high speed, although its performance in dealing diverse situations requires some improvement. We present a new tracking method whereby the reliability is greatly enhanced while maintaining its speed by integrating a Kalman filter with the KCF. The tracker works as follow. After the KCF estimates target’s position based on the prediction by the Kalman filter, then the estimated value is given to the updating step of the Kalman filter. During the KCF learning phase, the kernel model is updated using the correct state. Evaluation result using the standard tracking databases suggests that the present tracker outperforms the standard KCF, MOSSE and MIL trackers, respectively. In particular, it is the only tracker that can deal very well with the occlusion and human-crossing tasks, which are the crucial requirements for the high-end surveillance.