基于稀疏卷积特征和相关滤波的实时视觉跟踪算法

为提高分层卷积相关滤波视觉跟踪算法的实时性能，提出一种稀疏卷积特征的实时目标跟踪算法。首先，在分析不同层卷积特征的基础上，采用等间隔采样的方式提取每个卷积层的稀疏卷积特征；然后，对每个卷积层特征的相关滤波响应值进行加权组合，得到目标预测的位置；最后，采用稀疏的模型更新策略进一步提高算法的运行速度。在OTB-2015新增的50组数据上对所提算法进行测试，实验结果表明，该算法的平均距离精度为82.2%，比原分层卷积特征跟踪算法提高了5.25个百分点，对目标姿态以及遮挡等变化具有较好的鲁棒性。该算法的平均跟踪速度为32.6帧/s，是原分层卷积特征跟踪算法的近3倍，能达到实时跟踪的效果。

Real-time visual tracking algorithm based on correlation filters and sparse convolutional features

Concerning the real-time performance of the hierarchical convolutional features for visual tracking, a real-time object tracking algorithm based on sparse convolution features was proposed. By analyzing the characteristics of different convolution layers, the equidistant interval sampling method was adopted to extract the sparse convolutional features of each layer. Then the correlation filter response values of each convolutional feature were combined to estimate the location of target object. Finally, a sparse update strategy was applied to improve the computing speed. Experimental results on benchmark dataset OTB-2015 show that the average distance precision of the proposed algorithm is 82.2%, which is 5.25 percentage points higher than that of the original hierarchical convolutional feature tracking algorithm; furthermore, it has better robustness to appearance changes and occlusions. The average tracking speed of the proposed algorithm is 32.6 frames per second, which is nearly 2 times faster than before, which can achieve real-time performance.