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首页> 外文会议>Automatic target recognition XXIII >Unsupervised Pedestrian Detection Using Support Vector Data Description
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Unsupervised Pedestrian Detection Using Support Vector Data Description

机译：使用支持向量数据描述的无监督行人检测

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摘要

In this paper, an unsupervised pedestrian detection algorithm is proposed. An input image is first divided into overlapping detection windows in a sliding fashion and Histogram of Oriented Gradients (HOG) features are collected over each window using non-overlapping cells. A distance metric is used to determine the distance between histograms of corresponding cells in each detection window and the average pedestrian HOG template (determined a priori). These distances over a group of cells are concatenated to obtain the feature vector pertaining to a block of cells. The feature vectors over overlapping blocks of cells are concatenated to form the distance feature vector of a detection window. Each window provides a data sample and the data samples extracted from the whole image are then modeled as a normalcy class using Support Vector Data Description (SVDD). The benefit of using the state-of-the-art SVDD technique to model the normalcy class is that it can be controlled by setting an upper limit on the permissible outliers during the modeling process. Assuming that most of the image is covered by background, the outliers that are detected during the modeling of the normalcy class can be hypothesized as detection windows that contain pedestrians in them. The detections are obtained at different scales in order to account for the different sizes of pedestrians. The final pedestrian detections are generated by applying non-maximal suppression on all the detections at all scales. The system is tested on the INRIA pedestrian dataset and its performance analyzed with respect to accuracy and detection rate.
机译：本文提出了一种无监督的行人检测算法。首先以滑动方式将输入图像划分为重叠的检测窗口，然后使用非重叠单元在每个窗口上收集定向梯度直方图（HOG）特征。距离量度用于确定每个检测窗口中相应单元格的直方图与平均行人HOG模板（先验确定）之间的距离。将一组单元格上的这些距离连接起来，以获得与单元格块有关的特征向量。将单元的重叠块上的特征向量连接起来以形成检测窗口的距离特征向量。每个窗口提供一个数据样本，然后使用支持向量数据描述（SVDD）将从整个图像中提取的数据样本建模为常规类。使用最新的SVDD技术对法线类进行建模的好处是，可以通过在建模过程中设置允许的异常值上限来控制它。假设大多数图像都被背景覆盖，则可以将在常态类建模期间检测到的异常值假设为其中包含行人的检测窗口。为了解决行人的不同大小，以不同的比例获得了检测结果。最终行人检测是通过在所有尺度上对所有检测应用非最大抑制来生成的。该系统在INRIA行人数据集上进行了测试，并就准确性和检测率对性能进行了分析。

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