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首页> 外文会议>2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics >Deformable object tracking with spatiotemporal segmentation in big vision surveillance
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Deformable object tracking with spatiotemporal segmentation in big vision surveillance

机译：大视力监视中具有时空分割的可变形对象跟踪

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摘要

The rapid development of worldwide networks has changed many challenge problems from video level to big video level for vision based surveillance. An important technique for big video processing is to extract the salient information from the video datasea effectively. As a fundamental function for data analysis such as behavior understanding for social security, object tracking usually plays an essential role by separating the salient areas from the background scenarios in video. But object tracking in realistic environments is not easy because the appearance configuration of a realistic object may have continual deformation during the movement. In conventional online tracking-by-learning studies, fix-shape appearance modeling is usually utilized for training samples generation due to its applicable simplicity and convenience. Unfortunately, for generic deformable objects, this modeling approach may wrongly discriminate some background areas as the part of object, which is supposed to deteriorate the model update during online learning. Therefore, employing the object segmentation to obtain more precise foreground areas for learning sample generation has been proposed recently to resolve this problem, but a common limitation of these approaches is that the object segmentation was only performed in spatial domain rather than spatiotemporal domain of the video. Therefore, when the background texture is similar to the target object, tracking failure happens because accurate segmentation is hard to be achieved. In this paper, a motion-appearance model for deformable object segmentation is proposed by incorporating pixel based gradients flow in the spatiotemporal domain. With motion information between the consecutive frames, the irregular-shaped object can be accurately segmented by energy function optimization and boundary convergence and the proposed segmentation is then incorporated into a structural SVM tracking framework for online learning sample generation. We h- ve evaluated the proposed tracking on the benchmark video as well as the surveillance video datasets including heavy intrinsic variations and occlusions, as a demonstration, the experiment results has verified a significant improvement in tracking accuracy and robustness in comparison with other state-of-art tracking works.
机译：全球网络的快速发展已将许多挑战性问题从基于视频的监视的视频级别变为了大视频级别。大视频处理的一项重要技术是有效地从视频数据中提取显着信息。作为数据分析的基本功能，例如对社会保障的行为理解，对象跟踪通常通过将视频中的显着区域与背景场景分开来发挥重要作用。但是在现实环境中跟踪对象并不容易，因为现实对象的外观配置在移动过程中可能会持续变形。在常规的在线学习跟踪研究中，固定形状外观建模由于其适用的简单性和便利性，通常用于训练样本生成。不幸的是，对于通用的可变形对象，这种建模方法可能会错误地将某些背景区域区分为对象的一部分，这可能会破坏在线学习期间的模型更新。因此，最近提出了使用对象分割来获得更精确的前景区域以学习样本生成的方法来解决该问题，但是这些方法的共同局限性在于对象分割仅在视频的空间域而不是时空域中进行。 。因此，当背景纹理与目标对象相似时，由于难以实现精确的分割，因此会发生跟踪失败。在本文中，通过在时空域中合并基于像素的梯度流，提出了一种用于可变形对象分割的运动外观模型。利用连续帧之间的运动信息，可以通过能量函数优化和边界收敛来精确地分割不规则形状的对象，然后将所建议的分割方法合并到用于在线学习样本生成的结构化SVM跟踪框架中。我们已经评估了对基准视频以及包括大量固有变化和遮挡的监控视频数据集的拟议跟踪，作为演示，实验结果证明与其他状态相比，跟踪精度和鲁棒性有了显着提高艺术跟踪作品。

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