增量学习灰度与轮廓模板的行人跟踪方法

为了解决云台摄像机的行人跟踪问题,提出了一种基于粒子滤波的行人跟踪算法.该方法在目标灰度模板以外,学习并更新行人目标的轮廓模板.考虑到行人轮廓因为视角变化可能发生的突然改变,算法准备了多套从不同视角观测的轮廓模板,并且逐渐更新它们使之可以逐渐捕捉目标的轮廓特征.在多段云台摄像机拍摄的监控视频上测试了所提出的算法.实验结果显示,该算法比其他先进的跟踪算法有更长的准确跟踪时间.

Incremental Learning Intensity and Contour Templates for Tracking Pedestrians on PTZ Camera Surveillance Platform

This paper presents a novel particle-based pedestrian tracking algorithm for PTZ camera surveillance. Most of the state-of-art particle-based tracking algorithms are challenged due to lacking of a reliable moving object detection and drastic scale along with perspective shift of the target. Therefore, pure intensity based algorithms usually miss the target gradually without other features for correcting target location. Our method learns and maintains a contour template of the target besides intensity. Taking into account both the evolution and sudden change of the pedestrian contour, the proposed tracking algorithm maintains several sets of profiles from different perspectives and evolves them incrementally. The effectiveness of our tracking algorithm with extra contour measurement has been tested over several surveillance records captured from PTZ camera. Compared with other cutting edge tracking algorithms, this presented algorithm could estimate the target location more robustly.