基于运动行为的复杂监控事件探测

Complex Surveillance Events Detection Based on Motion Activity

摘要:

针对视频监控过程，使用运动目标的状态特征描述场景中存在的语义内容.基于DBSCAN聚类模型学习特征集的潜在结构，生成了运动行为模式集.使用高级Petri网刻画模式间的连续、并发等时序关系，构成复杂语义事件探测模型.无监督式的模式学习过程对低层噪声有较强的鲁棒性，而定性的事件描述模型对于高层事件的推理具有更强的灵活性.在实验中，通过聚类学习得到的行为模式，给出了事件Petri网的具体建模过程，并演示了“停留”与“偷车”两个感兴趣事件的探测结果.

Abstract:

For visual surveillance, the semantic content of video was modelled by the states of motion targets. Feature vectors were clustered based on DBSCAN to obtain activity patterns which represent potential structure of training set. Then a complex events detection model was investigated by ultilizing high level Petri nets to model the temporal dependence relationship of activity patterns, like continuum and concurrence. The unsupervised learning process of activity patterns was robust to low level noise. By combining Petri framework representation, inference of semantic events could be more flexibly. In the experiments, Petri nets modeling process was demonstrated based on the results of clustering and the validity was given by the detection of two interesting semantic events ‘staying’ and ‘stealing’.