Aiming at the problem that the traditional video monitoring method can not divide the dense foreground objects accurately,a multi-target video monitoring method based on Adaboost and codebook model was proposed.Firstly,the Adaboost human head classifier was obtained by training,and the background model was established for the vertical elevator image by the codebook algorithm.The foreground image was extracted and heads were detected and tracked.After that,the pedestrian targets were removed to get the object targets,and the object targets were tracked.Finally,the movement of pedestrians and objects was monitored.The experimental results on 12 entrance area videos show that the method can track pedestrians and objects accurately and stably.It can accomplish the monitoring tasks of retrograde detection,passenger statistics,pedestrian congestion and object retention.With the processing speed of 36 frames per second,the trackingaccuracy rate is above 94％ and the monitoring-accuracy rate is 95.8％.The proposed algorithm meets robustness,real-time and accuracy requirements of the intelligent video monitoring system.%针对传统视频监控方法无法对密集前景目标进行准确分割的问题,提出一种基于Adaboost和码本模型的多目标视频监控方法.首先,通过训练得到Adaboost人头分类器,利用码本算法为垂直拍摄的手扶电梯出入口图像建立背景模型,提取前景图像对其进行人头检测和跟踪;之后,剔除行人目标得到物件目标,对物件目标进行跟踪;最后,根据行人和物件的运动特征进行监控.对12段出入口视频序列的实验结果表明,监控方法能够准确稳定地跟踪行人和物件,完成逆行检测、客流统计、行人拥堵和物件滞留等监控任务,处理速度达到36帧/秒,目标跟踪准确事达到94％以上,行为监控准确率达到95.8％,满足智能视频监控系统鲁棒性、实时性和准确性的要求.
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