视觉自适应行人计数

Vision-based Adaptive Pedestrian Counting

基于视觉的行人计数技术因其广阔的应用前景逐步成为智能视觉监控领域的一个研究热点。本文提出了一种基于虚拟门上前景像素点个数的行人计数方法。该方法分为学习和计数两个过程。在学习过程中,本方法采用基于行人检测的方法获取场景中的若干行人模型,并利用线性拟合为虚拟门上的点赋予权重。在计数过程中,本方法在考虑虚拟门上前景像素的权重、运动矢量的大小和方向等信息的基础上,逐帧统计虚拟门上前景点个数,通过特定时间内累计的前景点数量来确定通过虚拟门的行人数量。实验表明,该方法能够在保证计数精度的前提下,有较好的实时性能。

Pedestrian counting is recently focused by many researchers in intelligent visual surveillance domain because of its wide applications. A novel method for pedestrian counting is proposed based on the foreground pixels on virtual gate. The proposed method is composed of two processes which are adaptive learning and counting. In the learning stage, many pedestrian models in the current scene are firstly extracted using a pedestrian detection method based on HOG, and then these models are used to fit a line which can be used to determine the weight of every point. In the counting stage, we get the foreground pixels on virtual gate as well as their weights, motion vectors at each frame, and the amount of pedestrian passing the virtual gate can be obtained by accumulating all those moving pixels. The experimental results show that our method has real-time performance under the premise of counting precision.