Combined shape and feature-based video analysis and its application to non-rigid object tracking
Combined shape and feature-based video analysis and its application to non-rigid object tracking
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DOI：
10.1049/iet-ipr.2009.0276
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发表时间：
2011-02
影响因子：
2.3
通讯作者：
Tae-Yong Kim;Seong-Won Lee;J. Paik
中科院分区：
文献类型：
--
作者：
Tae-Yong Kim;Seong-Won Lee;J. Paik
由于块匹配算法（BMA）计算结构简单、性能稳定，许多视频对象跟踪系统都采用它。然而，BMA表现出由非刚性形状和与背景相似的图案引起的基本限制。提出了一种基于形状和特征的非刚性目标跟踪算法，该算法与自适应背景生成紧密耦合，以克服块匹配的限制。该算法对目标的突然运动或特征的变化具有较强的鲁棒性。通过跟踪特征点及其相邻区域，这成为可能。结合背景信息和形状边界信息，可以很容易地找到目标对象及其边界上的特征点，从而显著提高了跟踪性能;形状控制点（SCP）在目标轮廓上规则分布，跟踪过程中比较并更新质心，去除偏离的SCP，只跟踪合格的SCP。因此，所提出的方法变得免费的潜在失败因素，如时空相似性之间的对象和背景，对象变形和遮挡，仅举几例。实验已经进行了使用几个内部的视频序列，包括各种对象，如移动的机器人，游泳的鱼和步行的人。为了证明所提出的跟踪算法的性能，在噪声和低对比度环境下进行了大量的实验。为了进行更客观的比较，还使用了2002年跟踪监视数据集的性能评估。
Many video object tracking systems use block matching algorithm (BMA) because of its simple computational structure and robust performance. The BMA, however, exhibits fundamental limitations resulting from non-rigid shapes and similar patterns to the background. The authors propose a combined shape and feature-based non-rigid object tracking algorithm, which is tightly coupled with an adaptive background generation to overcome the limit of block matching. The proposed algorithm is robust to the object's sudden movement or the change of features. This becomes possible by tracking both feature points and their neighbouring regions. Combination of background and shape boundary information significantly improves the tracking performance because the target object and the corresponding feature points on the boundary can be easily found. The shape control points (SCPs) are regularly distributed on the contour of the object, and the authors compare and update the centroid during the tracking process, where straying SCPs are removed, and the tracking continues with only qualified SCPs. As a result, the proposed method becomes free from potential failing factors such as spatio-temporal similarity between object and background, object deformation and occlusion, to name a few. Experiments have been performed using several in-house video sequences including various objects such as a moving robot, swimming fish and walking people. In order to demonstrate the performance of the proposed tracking algorithm, a number of experiments have been performed under noisy and low-contrast environment. For more objective comparison, performance evaluation of tracking surveillance 2002 data sets were also used.