具备重检测机制的融合特征视觉跟踪算法

结合图像梯度特征和颜色特征，在相关滤波器跟踪框架基础上，提出一种改进的 视觉跟踪算法。对颜色特征进行统计建模，结合由稠密目标后验概率积分得到的目标置信积分 和梯度特征相关滤波输出作目标跟踪。同时，还对目标跟踪的结果作质量评估，在跟踪质量非 可靠时启动目标重检测过程，采用基于稠密目标后验概率的置信积分来确定备选目标。对跟踪 质量不可靠且未重检测到可靠目标的视频帧，不进行跟踪模型的在线更新。实验表明，该算法 可以有效避免因遮掩等原因而引起的跟踪不可靠和模型漂移的问题，跟踪性能和几个主流的相 关滤波类跟踪器相比有明显改善。

Features Integration Based Visual Tracking with Re-Detection Method

Taking the gradient features and color features of an image into consideration, an improved visual tracking algorithm based on correlation filter tracking is proposed in this paper. The algorithm uses the Bayesian theory to model the color information, and carries out object tracking with the combination of gradient features’ correlation filter output and object confidence integral map which is obtained from dense object posterior probability. Meanwhile, the algorithm conducts a quality assessment of the results of the object tracking. Once the tracking quality turns out to be unreliable, the object re-detection process will start, which is based on the object confidence integral to determine the candidate objects. As to the video frame with unreliable tracking quality or no reliable object after re-detection, the tracking model will not be updated online. Experiments show that the proposed algorithm can effectively avoid the problem of unreliable tracking and model drift caused by circumstances varying, and its tracking performance is obviously improved compared with those of several state-of-the-arts correlation filters.