LU Li,XU Yuan-ping,LU Jun,et al.A Crowd Behavior Model based on an ImprovedSocial Force Anomaly Detection Algorithm[J].Journal of Chengdu University of Information Technology,2018,(01):1-7.[doi:10.16836/j.cnki.jcuit.2018.01.001]
基于社会力异常检测改进算法的人群行为模型
- Title:
- A Crowd Behavior Model based on an ImprovedSocial Force Anomaly Detection Algorithm
- 文章编号:
- 2096-1618(2018)01-0001-07
- Keywords:
- social force; crowd anomaly detection; unsupervised algorithm; trajectory clustering; crowd behavior; deep learning; video monitoring
- 分类号:
- TP317.4
- 文献标志码:
- A
- 摘要:
- 社会力异常检测算法(SAFM)是检测人群异常行为(人群聚集和恐慌逃散等)的一种核心算法,提取的底层特征不能完整地描述人群的运动状态,导致人群异常行为的识别率低。为此,提出一种改进的社会力异常检测算法(SFDE)解决此问题。算法引入人群运动的轨迹避免底层特征的丢失,通过无监督方法将轨迹进行聚类,再通过轨迹和人群相互作用力建立人群行为模型(人群运动强度、人群方向熵和聚簇中心距离势能的作用力)。为证明算法有效性,应用改进的SFDE算法结合深度学习模型来识别不同的人群异常行为。通过UMN数据对算法进行验证,结果表明SFDE算法的准确率比传统的SAFM算法提高了18%,并且执行时间提高了2.2 s。
- Abstract:
- Social Force anomaly detection algorithm(SAFM)is a core algorithm for detecting abnormal crowd behaviors(e.g.,crowd aggregations and panic escapes,etc.).Some low-level features of the algorithm can’t fully describe the movement states of the crowd,so the classification recognition rate is very low.Thus,an improved social force anomaly detection algorithm(SFDE)is proposed in this research to solve this problem.This algorithm introduces the trajectories to avoid the loss of low-level features, and it also groups trajectories into clusters by applying an unsupervised algorithm. Finally,a model of crowd behavior can be established through combination of trajectories and multiple crowd forces(e.g.,the kinetic energy of the crowd,the entropy of motion direction and the force of cluster centers).To test the validity and effectiveness of the proposed algorithm,this paper presents how to apply SFDE together with the deep learning model to recognize various crowd behaviors.The SFDE has been tested and evaluated by using the UMN dataset.Experimental results show that the accuracy of SFDE is 18% higher than SAFM,and the execution time is decreased by 2.2 s.
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备注/Memo
收稿日期:2017-09-04基金项目:国家自然科学基金资助项目(61203172、61202250); 四川省科技厅资助项目(2017JY0011、2014GZ0007); 深圳重大国际合作资助项目(GJHZ20160301164521358)