融合前景判别和圆形搜索的目标跟踪算法

针对运动目标在发生遮挡、形变、旋转和光照等变化时会导致跟踪误差大甚至丢失目标以及传统跟踪算法实时性差的问题，提出了一种融合前景判别和圆形搜索（CS）的目标跟踪算法。该算法采用了图像感知哈希技术来描述与匹配跟踪目标，跟踪过程使用了两种跟踪策略相结合的方法，能够有效地解决上述问题。首先，根据目标运动方向的不确定性和帧间目标运动的缓慢性，通过CS算法搜索当前帧局部（目标周围）最佳匹配位置；然后，采用前景判别PBAS算法搜索当前帧全局最优目标前景；最终，选取两者与目标模板相似度更高者为跟踪结果，并根据匹配阈值判断是否更新目标模板。实验结果表明，所提算法在精度、准确率和实时性上都比MeanShift算法更好，在目标非快速运动时有较好的跟踪优势。

Object tracking based on foreground discrimination and circle search

Aiming at the problems of low accuracy and even object lost in moving object tracking under occlusion, deformation, rotation, and illumination changes and poor real-time performance of the traditional tracking algorithm, a target tracking algorithm based on foreground discrimination and Circle Search (CS) was proposed. The image perceptual hashing technique was used to describe and match tracked object, and the tracking process was based on the combination of the above was tracking strategies, which could effectively solve the above problems. Firstly, because the direction of motion uncertain and the inter-frame motion was slow, CS algorithm was used to search the local best matching position (around the tracked object) in the current frame. Then, the foreground discrimination PBAS (Pixel-Based Adaptive Segmenter) algorithm was adopted to search for the global optimal object foreground in the current frame. Finally, the one with higher similarity with the object template was selected as the tracking result, and whether to update the target template was determined according to the matching threshold. The experimental results show that the proposed algorithm is better than the MeanShift algorithm in precision, accuracy, and has a better tracking advantage when the target is not moving fast.