Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the ...Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.展开更多
Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is s...Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is still a challenging task. In this paper, we propose a real-time compressive tracking based on online Hough forest. The gray and texture features of discrete samples are extracted and compressed via the random measurement matrix. Online Hough forest classifier is used to vote the location probability of the target, and it optimizes the confidence map estimation for the target detection. The location of target being tracked is determined by combining the upper frame of the target center location and the probability confidence map of the incremental Hough forest. Finally, the classifier parameters are updated online by introducing the illumination variation and target occlusion feedback mechanism adaptively. The experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm can effectively enhance the robustness and accuracy, and inherit the real-time performance of the compressive tracking algorithm.展开更多
针对快速压缩跟踪算法(FCT)分类器参数更新盲目、目标尺寸固定和未能跟踪目标完全遮挡再出现的问题,提出一种融合感知哈希的快速压缩跟踪算法(Fast compressive tracking algorithm based on perceptual hashing,PH-FCT).首先,使用压缩...针对快速压缩跟踪算法(FCT)分类器参数更新盲目、目标尺寸固定和未能跟踪目标完全遮挡再出现的问题,提出一种融合感知哈希的快速压缩跟踪算法(Fast compressive tracking algorithm based on perceptual hashing,PH-FCT).首先,使用压缩特性构建目标和背景的贝叶斯分类器,同时生成目标的感知哈希描述子;使用分类器获得下一帧响应值最高的样本,以样本为中心采集不同尺寸区域,计算它们与目标的汉明距离,若最小汉明距离小于阈值,则视当前尺寸区域为目标区域,更新目标信息(目标位置、尺寸和感知哈希描述子)与分类器参数,并标记当前帧检测到目标,否则不更新且标记当前帧未检测到目标.当上一帧被标记为未检测到目标,则当前帧使用全图等间隔采样,样本个数与FCT算法粗采样一致,使用分类器得出响应值最高的样本,再以该样本中心为圆心,半径为5的圆形区域遍历精确采样,得出最有可能是目标的样本,最后通过判断汉明距离决定是否更新参数.实验结果表明,该算法在抗遮挡性、有效性和鲁棒性上优于FCT算法,且拥有较好的目标自找回能力,为目标的快速跟踪提供一种新的方法.展开更多
文摘Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.
基金supported by National Natural Science Foundation of China(No.61203343)Natural Science Foundation of Hebei Province(No.E2014209106)+1 种基金Science and Technology Research Project of Hebei Provincial Department of Education(Nos.QN2016102 and QN2016105)the Graduate Student Innovation Fund of North China University of Science and Technology(No.2016S10)
文摘Existing tracking algorithms often suffer from the drift and lost problems caused by factors such as pose variation, illumination change, occlusion and motion. Therefore, developing a robust and effective tracker is still a challenging task. In this paper, we propose a real-time compressive tracking based on online Hough forest. The gray and texture features of discrete samples are extracted and compressed via the random measurement matrix. Online Hough forest classifier is used to vote the location probability of the target, and it optimizes the confidence map estimation for the target detection. The location of target being tracked is determined by combining the upper frame of the target center location and the probability confidence map of the incremental Hough forest. Finally, the classifier parameters are updated online by introducing the illumination variation and target occlusion feedback mechanism adaptively. The experiments with state-of-the-art algorithms on challenging sequences demonstrated that the proposed algorithm can effectively enhance the robustness and accuracy, and inherit the real-time performance of the compressive tracking algorithm.
文摘针对快速压缩跟踪算法(FCT)分类器参数更新盲目、目标尺寸固定和未能跟踪目标完全遮挡再出现的问题,提出一种融合感知哈希的快速压缩跟踪算法(Fast compressive tracking algorithm based on perceptual hashing,PH-FCT).首先,使用压缩特性构建目标和背景的贝叶斯分类器,同时生成目标的感知哈希描述子;使用分类器获得下一帧响应值最高的样本,以样本为中心采集不同尺寸区域,计算它们与目标的汉明距离,若最小汉明距离小于阈值,则视当前尺寸区域为目标区域,更新目标信息(目标位置、尺寸和感知哈希描述子)与分类器参数,并标记当前帧检测到目标,否则不更新且标记当前帧未检测到目标.当上一帧被标记为未检测到目标,则当前帧使用全图等间隔采样,样本个数与FCT算法粗采样一致,使用分类器得出响应值最高的样本,再以该样本中心为圆心,半径为5的圆形区域遍历精确采样,得出最有可能是目标的样本,最后通过判断汉明距离决定是否更新参数.实验结果表明,该算法在抗遮挡性、有效性和鲁棒性上优于FCT算法,且拥有较好的目标自找回能力,为目标的快速跟踪提供一种新的方法.
