基于空间虚拟墙的行人越界异常行为自动识别

提出了基于空间虚拟墙的行人越界异常行为自动识 别方法。基于人头刚体不变性及其个体类Haar特征的差异性,融合级联分类器与粒子滤波动 态跟 踪链,实现视频场景下的人头 目标跟踪与定位。进而基于人体身高不变性,建立基于行人头顶的三维平面方程及 其视频监控场景下的空间虚拟墙, 从而将行人跨越二维场景警戒线问题,转化为行人穿越三维空间虚拟警戒墙,实现行人是否 越界的有效判断。通过在不同视 频场景的实验验证与对比,结果表明,所提方法有效、可行,无需特定的硬件支持以及场景 条件约束。

Recognition on pedestrian cross-border abnormal behavior based on a spatial vir tual wall

Aiming at some limits in cross-border abnormal behavior recognition f or pedestrian in a video surveillance scenario at present,a novel approach to recognizing the cross-border abnormality has be en developed based on a spatial virtual wall.There are some rigid invariances and differences in the Haar-like features for the head. A cascaded classifier and dynamic tracking chain with the particle filtering are fused to track and locate the head as a target from the v ideo surveillance scene.Both three-dimensional plane equation for the top of the head and its corresponding spatial virtual wall in t he surveillance scene are constructed based on the height-invariance.The problem whether a pedestrian spans a warning line in a t wo-dimensional scene is transformed to the one whether he passes through the three-dimensional virtual warning wall.It is est imated efficiently if a pedestrian crosses the border or not.Some state-of-the-arts and experiments have been done in some video scen es with different contents to test the performance in the same conditions.Experimental results show that the proposed meth od is efficient and valid without any specific hardware support or conditional constraint in the scenario.