Conventional tracking approaches track objects using a rectangle bounding box. Gait, gesture and many medical analyses require non-rigid shape extraction. A non-rigid object tracking is more difficult because it needs more accurate object shape and background separation in contrast to rigid bounding boxes. Active contour plays a vital role in the retrieval of image shape. However, the large computation time involved in contour tracing makes its use challenging in video processing. This paper proposes a new formation of the region-based active contour model (ACM) using a mean-shift tracker for video object tracking and its shape retrieval. The removal of re-initialization and fast deformation of the contour is proposed to retrieve the shape of the desired object. A contour model is further modified using a mean-shift tracker to track and retrieve shape simultaneously. The experimental results and their comparative analysis concludes that the proposed contour-based tracking succeed to track and retrieve the shape of the object with 71.86% accuracy. The contour-based mean-shift tracker resolves the scale-orientation selection problem in non-rigid object tracking, and resolves the weakness of the erroneous localization of the object in the frame by the tracker.

中文翻译：

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一种基于区域的快速活动轮廓，用于非刚性对象跟踪及其形状检索

传统的跟踪方法使用矩形边界框跟踪对象。步态，手势和许多医学分析都需要非刚性的形状提取。与刚性边界框相比，非刚性对象跟踪更加困难，因为它需要更准确的对象形状和背景分离。活动轮廓在图像形状的检索中起着至关重要的作用。但是，轮廓跟踪涉及的大量计算时间使其在视频处理中的使用具有挑战性。本文提出了一种新的基于均值漂移跟踪器的区域主动轮廓模型（ACM），用于视频对象跟踪及其形状检索。建议取消轮廓的重新初始化和快速变形以恢复所需对象的形状。使用均值漂移跟踪器进一步修改轮廓模型，以同时跟踪和检索形状。实验结果及其比较分析得出的结论是，基于轮廓的跟踪成功地以71.86％的精度跟踪和检索了对象的形状。基于轮廓的均值移动跟踪器解决了非刚性对象跟踪中的缩放方向选择问题，并解决了跟踪器对对象在框架中错误定位的弱点。