视频异常行为识别与分级预警系统

参考格灵深瞳分级评价体系并引入迟滞比较器相关思想,结合最近邻与SVM双层分类器学习,建立了针对目标入侵、目标高速运动、目标遗留物与人群聚集逃离、人群打架斗殴、人群骚乱六种常见目标异常行为的自动分类与分级预警系统。1提出并实现了一套较完备的异常行为分级预警系统;2在行为分析之前以人群密度与能量为特征引入最近邻分类器实现个体行为与群体行为的预分类;3通过引入迟滞比较器实现高速运动行为的稳定预警;且该方法具有一定普及意义。分别在标准库和自行拍摄的视频上进行实验验证。实验证明,该系统能够稳定实现对上述六种普遍异常行为的分类分级预警,实现了群体分析与个体分析、检测与识别、分类与预警的一体化。

Ranked-warning system on identification of abnormal behavior in video monitoring

This paper takes into account grid deep spiritual pupil grade-evaluation system and main ideas of hysteresis comparator, combined with the nearest neighbor classifier and SVM learning, which aims to establish an automatic Classification and Grading Warning System recognizing six common target abnormal behavior including invasion ,fast-moving , remnants, fleeing, fights and riots . This paper contributes at following three aspects: firstly, we propose a comprehensive system dealing with classification and warning of abnormal behavior. Secondly, before recognizing abnormal behavior the population density and the energy is characterized, which are input of the nearest neighbor classifier, achieving pre-classification of individual behavior and group behavior . Thirdly, this paper tends to achieve stable behavior warning by introducing hysteresis comparator, and this method has certain universal significance. In this paper, experiments are carried out on the standard library and video sets shoot by our own. Experimental results show that the system can achieve high warning and classification stability of the six abnormal behavior, which integrates self-analysis and analysis groups, detection and identification, classification and warning together.