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首页> 外文会议>2013 Seventh International Conference on Distributed Smart Cameras >Detection of moving people with mobile cameras by fast motion segmentation
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Detection of moving people with mobile cameras by fast motion segmentation

机译：通过快速运动分割检测移动摄像机中的移动人员

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摘要

Detecting humans from video sequences is a key and difficult problem in computer vision. The problem becomes even more challenging when the camera is mobile, and thus, the background is not static. In this case, the traditional approach of background subtraction cannot be employed. Methods based on Histogram of Oriented Gradients (HOG) have been introduced and widely used for human detection due to reliable performance. Yet, these sliding window-based algorithms are computationally expensive to run on embedded platforms. In order to address this problem, we present a significantly faster moving human detection method that is based on fast motion segmentation, and uses HOG as the feature descriptor. First, edges are detected on two consecutive frames, and the difference between the edge images is computed. Then, a new edge-based frame alignment method is used to find the global minimum for motion compensation, and segment the motion region of interest (ROI). Instead of searching for the human(s) in the whole frame, the HOG features in sliding windows are calculated only in the ROI. Finally, Support Vector Machine (SVM) is used to classify human and non-human regions. Compared to the traditional method of using HOG and searching the whole frame, the proposed method significantly reduces the search region by motion segmentation, and speeds up the detection process. Experiments have been performed on three different scenarios, and the results show that the reduction in execution time of a frame can reach as high as 95.83%.
机译：从视频序列中检测人类是计算机视觉中的关键和难题。当照相机是可移动的时，该问题变得更加具有挑战性，因此背景不是静态的。在这种情况下，不能采用传统的背景扣除方法。由于性能可靠，基于定向直方图（HOG）的方法已被引入并广泛用于人体检测。然而，这些基于滑动窗口的算法在嵌入式平台上运行在计算上是昂贵的。为了解决这个问题，我们提出了一种基于快速运动分割并使用HOG作为特征描述符的移动速度更快的人体检测方法。首先，在两个连续的帧上检测边缘，并计算边缘图像之间的差异。然后，使用一种新的基于边缘的帧对齐方法来找到用于运动补偿的全局最小值，并分割感兴趣的运动区域（ROI）。代替在整个帧中搜索人员，仅在ROI中计算滑动窗口中的HOG特征。最后，支持向量机（SVM）用于对人类和非人类区域进行分类。与传统的使用HOG和搜索整个帧的方法相比，该方法通过运动分割显着减少了搜索区域，并加快了检测速度。在三种不同的场景下进行了实验，结果表明，一帧的执行时间减少可以达到95.83％。

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