基于双层粒子滤波和半监督Hough Forests 的多目标跟踪

本文针对单目摄像头、复杂可变背景环境下的多目标跟踪问题,将 tracking-by- detection 方法与粒子滤波相结合,从不稳定的信息源中提取高置信度模型作为观测,在半监督学习框架中实现了动态视频场景中的多个目标跟踪,并设计了一个多目标的维护机制以应对遮挡、背景变化、目标进出场景等可能引起目标混淆的情况.实验证明,本文提出的算法能够稳定跟踪复杂场景中的多个目标,有效区分不同目标,对目标的遮挡、背景干扰等均有良好的处理效果.

Multi-objects Tracking with Dual-level Particle Filter Embedded Semi-supervised Hough Forests

This paper focuses on multi-objects tracking problems in monocular camera and mutative background complex scenes. We achieve multi-objects tracking in dynamical scenes under a semi-supervised learning framework, which combines tracking-by-detection method and particle filter together, integrates unreliable information sources and extracts a high-confidence observation model from it. Then an objects maintenance scheme is carried out to cope with occlusion, background changing, entry/exit and so on. The results on standard datasets demonstrate advantages of the proposed algorithm in complex environment, particularly on scenes with occlusion and obstruction.