Graph formulation of video activities for abnormal activity recognition

异常活动识别的视频活动图式

Pattern Recognition, Volume 65, May 2017, Pages 265-272

Dinesh Singh, C. Krishna Mohan

摘要：Abnormal activity recognition is a challenging task in surveillance videos. In this paper, we propose an approach for abnormal activity recognition based on graph formulation of video activities and graph kernel support vector machine. The interaction of the entities in a video is formulated as a graph of geometric relations among space–time interest points. The vertices of the graph are spatio-temporal interest points and an edge represents the relation between appearance and dynamics around the interest points. Once the activity is represented using a graph, then for classification of the activities into normal or abnormal classes, we use binary support vector machine with graph kernel. These graph kernels provide robustness to slight topological deformations in comparing two graphs, which may occur due to the presence of noise in data. We demonstrate the efficacy of the proposed method on the publicly available standard datasets viz. UCSDped1, UCSDped2 and UMN. Our experiments demonstrate high rate of recognition and outperform the state-of-the-art algorithms.

Surveillance scene representation and trajectory abnormality detection using aggregation of multiple concepts

基于多概念集结的监控场景表示与轨迹异常性检测

Expert Systems with Applications, Volume 101, 1 July 2018, Pages 43-55

Sk. Arif Ahmed, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy

摘要：Use of CCTV is growing rapidly in surveillance applications. Rapid advancement in machine learning and camera hardware has opened-up adequate scopes to build next generation of expert systems aiming at understanding surveillance environments automatically by detection of trajectory abnormality through analyzing object behavior. Such intelligent surveillance systems should be able to learn and combine multiple concepts of abnormality in real-life scenario and classify the events of interest as normal or abnormal. Primary challenges of such systems are to represent and learn patterns in surveillance scenes and combine multiple concepts of abnormalities to activate the alarm system.

This paper presents a graph-based representation of a given surveillance scene and learning of relevant features including origin, destination, path, speed, size, etc. These features are combined and correlated with target behaviors to detect abnormalities in moving object trajectories. We also propose an aggregation method that reduces the number of missed alarms during aggregation. Several cases using publicly available surveillance video datasets have been presented and the results indicate that the proposed method can be useful to design intelligent and expert surveillance systems.

The detecting of abnormal crowd activities based on motion vector

基于运动矢量的异常群体活动探测

Optik, Volume 166, August 2018, Pages 248-256

Wei Yan, Zheng Zou, Jianbin Xie, Tong Liu, Peiqin Li

摘要：Aiming at the crowd in high-definition video motion state of sudden changes in rapid detection of abnormal crowd behavior problem, this paper proposes a kind of abnormal behavior crowd detection method based on motion vector. This algorithm is established upon the Social Force Model, first, extracts the motion vector in the code stream of the high-definition compressed videos, computes the interaction force in the social force model and rapidly draws the characteristics of the moving crowd; then according to the algorithm, we perform the bag of words approach and histogram statistics on the intensity and angle of the interaction force flow; finally we analyze two histograms to distinguish the moving state of the crowd and fulfill the detection of the abnormal crowd movement. The simulation experiment shows the method compared with the traditional social force model, in the 1024 × 768HD video frame processing speed on the average increase of 30% in average, the discrimination of abnormal frame advance 35 frames, the recall to an average increase of 22%.

Intelligent video surveillance for real-time detection of suicide attempts

自杀企图实时侦查智能视频监控

Pattern Recognition Letters, Volume 110, 15 July 2018, Pages 1-7

Wassim Bouachir, Rafik Gouiaa, Bo Li, Rita Noumeir

摘要：Suicide by hanging is a sentinel event and a major cause of death in prisons, with an increasing frequency over recent years. The rapid detection of suicidal behavior can reduce the mortality rate and increase the odds of survival for the suicide victim. Significant efforts have been made to develop technologies for preventing hanging attempts, but most of them use cumbersome devices, or they are mainly depending on human attention and intervention. In this paper, we propose a vision-based method to automatically detect suicide by hanging. Our intelligent video surveillance system operates using depth streams provided by an RGB-D camera, regardless of illumination conditions. The proposed algorithm is based on the exploitation of the body joints’positions to model suicidal behavior. Both dynamic and static pose characteristics are calculated in order to efficiently capture the body joints’movement and model suicidal behavior. Results from the experiments on realistic video sequences, show that our system achieves a high accuracy in detecting suicide attempts, while meeting real-time requirements.