融合CN跟踪算法改进的TLD实时目标跟踪算法

针对TLD算法跟踪框在目标非刚性形变、旋转、背景杂乱等情景中容易导致跟踪漂移的问题,提出了一种融合CN跟踪算法改进的TLD实时目标跟踪算法(TLD-CN)。首先对跟踪框内区域计算图像显著性得到BRISK算法采样特征点的阈值,获得合适的特征点以建立旋转和尺度归一化的描述子,再融合颜色特征和纹理特征对前后帧跟踪框内描述子进行最优相似性匹配,得到匹配的特征点集合,对集合内特征点进行判别式字典的稀疏编码后,分别与CN跟踪框和TLD跟踪框的中心像素点进行相似度的度量,得到输出框调整的权重系数。实验结果表明,TLD-CN跟踪算法通过特征点度量出2种算法融合的权重值调整输出框,在目标形变、旋转、背景杂乱、快速运动等复杂跟踪情景中,具有很高的精度和成功率。权重系数自适应更新也避免了模型过拟合,达到实时跟踪效果。     

融合TLD框架的DSST实时目标跟踪改进算法

In view of the image blurring caused by fast target movement, it is difficult for the DSST algorithm to distinguish between the target and the background information. The filter is cyclically shifted during the training phase to collect dense samples, which easily results in boundary effect and leads to the tracking drift problem. Therefore, this paper proposes an improved DSST real-time target trac- king algorithm (TLD-DSST) that incorporates the TLD framework. The algorithm improves the position filter of the DSST algorithm, adds the weight coefficient matrix through the spatial regularization me- thod to reduce the response of the non-target area, and performs rough positioning of the target under fast motion. At the same time, a naive Bayesian classifier is introduced to improve the TLD detector, in order to improve the detector's ability to distinguish between the target and the background information. Moreover, the optimal similarity matching is performed on the position of the DSST target response and the target area obtained by the TLD detector, so as to get the precise positioning result. The TLD detector positive and negative sample online update mechanism is used to continuously optimize the robustness of the algorithm. Experimental results show that the TLD-DSST algorithm has high accuracy and success rate for target tracking in complex scenarios such as fast motion.