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Pedestrian Detection Based on Redundant Wavelet Transform

机译：基于冗余小波变换的行人检测

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摘要

Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.
机译：智能视频监视旨在分析由固定或移动监视摄像机捕获的视频或图像序列，包括运动对象检测，分割和识别。通过使用它，可以在异常情况下立即通知我们。行人检测在智能视频监控系统中起着重要的作用，也是智能车辆领域的关键技术。因此，行人检测在交通管理优化，安全预警和异常行为检测中具有至关重要的意义。通常，行人检测可以归纳为：首先估计移动区域；然后提取感兴趣区域的特征；最后使用分类器进行分类。冗余小波变换（RWT）克服了离散小波变换移位变量的不足，与离散小波变换相比，在运动估计方面具有更好的性能。为了解决不同速度下的多行人检测问题，我们提出了一种基于运动估计的行人检测算法，该算法结合了定向梯度直方图（HOG）和支持向量机（SVM），利用RWT进行运动估计。首先，使用RWT估算三个运动强度（IoM），并对相应区域进行分段。根据不同的IoM，将生成区域建议（RP）。然后，使用HOG提取RP的特征。最后，将特征输入到行人数据库训练的SVM中，并获得最终的检测结果。实验表明，该算法能够准确有效地检测出行人。

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