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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Compressive tracking via appearance modeling based on structural local patch
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Compressive tracking via appearance modeling based on structural local patch

机译：通过基于结构局部补丁的外观建模进行压缩跟踪

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摘要

In this paper, we propose a compressive tracking method via appearance model based on structural local patchs and improved Haar-like feature. In contrast to previous compressive tracking only considering the holistic representation, an object can be represented by local image patches with spatial layout in an object. This representation takes advantage of both partial information and spatial information of the target. Each local patch has a fixed position in the target field, and all local patches can represent the whole target. In addition, our appearance model based on features extracted from image patches, which can guarantee the randomness of the rectangular boxes and the distribution of the rectangular boxes over the entire image area, avoiding the randomness of the rectangular boxes is too strong to weak the feature expression. We sample the positive and negative samples and divide them into patchs to train a binary classification via a naive Bayes classifier with online update, then the classifier is used to discriminate the candidate samples. The candidate sample which gets the highest classify score is the target. After that we draw positive and negative samples in the same way with the candidate samples to update the classifier to get ready for next frame. Our approach helps not only locate the target more accurately but also can handle partial occlusion effectively. The proposed tracker is compared with several state-of-the-art trackers on some challenging video sequences. Our proposed tracker is better and more stable in both quantitative and qualitative comparisons.
机译：在本文中，我们提出了一种基于外观模型的压缩跟踪方法，该方法基于结构局部补丁和改进的类似Haar的特征。与仅考虑整体表示的先前压缩跟踪相反，可以通过在对象中具有空间布局的局部图像块来表示对象。该表示利用了目标的部分信息和空间信息。每个本地补丁在目标字段中都有固定的位置，所有本地补丁都可以代表整个目标。另外，我们的外观模型基于从图像补丁中提取的特征，可以保证矩形框的随机性和矩形框在整个图像区域的分布，避免矩形框的随机性过强而削弱该功能表达。我们对正样本和负样本进行采样，然后将其分为补丁，通过朴素的贝叶斯分类器使用在线更新来训练二进制分类，然后使用分类器来区分候选样本。得分最高的候选样本是目标。之后，我们以与候选样本相同的方式绘制正样本和负样本，以更新分类器以准备下一帧。我们的方法不仅有助于更准确地定位目标，而且可以有效地处理部分遮挡。在一些具有挑战性的视频序列上，将建议的跟踪器与几个最新的跟踪器进行了比较。我们提出的跟踪器在定量和定性比较方面都更好，更稳定。

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