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首页> 外文期刊>Journal of visual communication & image representation >Adaptive multi-modal stereo people tracking without background modelling
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Adaptive multi-modal stereo people tracking without background modelling

机译：无需背景建模的自适应多模式立体声人物跟踪

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Detecting and tracking persons in the sequences of monocular images are the important and difficult problems in computer vision and have been well studied in these two decades. Recently, the methods based on stereo vision have attracted great attentions since 3D information can be exploited. This paper presents an approach for multiple-people detection and tracking using stereo vision. Tracking is carried out using a multiple particle filtering approach that combines depth, colour and gradient information. We modify the degree of confidence assigned to depth information, according to the amount of it found in the disparity map, using a novel confidence measure. The greater the amount of disparity information found, the higher the degree of confidence assigned to depth information in the final particles weights is. In the worst case (total absence of disparity), the proposed algorithm makes use of the information available (colour and gradient) to track, thus performing as a pure colour-based tracking algorithm. People are detected combining an adaboost classifier with stereo information. In order to test the validity of our proposal, it is evaluated in several sequences of colour and disparity images where people interact in complex situations: walk at different distances, shake hands, cross their paths, jump, run, embrace each other and even swap their positions quickly trying to confuse the system. The experimental results show that the proposal is able to deal with occlusions and to effectively determine both the 3D position of the people being tracked and their 2D head locations in the camera image, and everything is realized in real time. Besides, as the proposed method does not require the use of a background model, it can be considered particularly appropriate for applications that must run on mobile devices.
机译：在单眼图像序列中检测和跟踪人物是计算机视觉中的重要和困难问题，并且在这两个十年中已经进行了深入研究。近年来，由于可以利用3D信息，因此基于立体视觉的方法引起了极大的关注。本文提出了一种使用立体视觉进行多人检测和跟踪的方法。使用结合深度，颜色和渐变信息的多粒子过滤方法进行跟踪。我们使用新颖的置信度度量，根据在视差图中发现的深度信息来修改分配给深度信息的置信度。发现的视差信息量越大，最终粒子权重中分配给深度信息的置信度越高。在最坏的情况下（完全没有视差），所提出的算法会利用可用的信息（颜色和渐变）进行跟踪，从而作为基于纯颜色的跟踪算法执行。通过将adaboost分类器与立体声信息相结合来检测人。为了测试我们的建议的有效性，我们对彩色和视差图像进行了一系列排序，以使人们在复杂的情况下进行交互：在不同距离处行走，握手，交叉，跳跃，奔跑，互相拥抱甚至交换他们的位置迅速试图混淆系统。实验结果表明，该方案能够处理遮挡并有效地确定被跟踪人员的3D位置及其在摄像头图像中的2D头部位置，并且所有这些操作都是实时实现的。此外，由于所提出的方法不需要使用背景模型，因此可以认为它特别适合必须在移动设备上运行的应用程序。

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