Motion Direction Inconsistency-Based Fight Detection for Multiview Surveillance Videos

Chuang Yao,Jiankang Ren,Xiaoyan Su,Xuehua Wang,Jun Zhang,Xinyi Kang,Keping Yu

doi:10.1155/2021/9965781

Chuang Yao, Jiankang Ren + Show 5 more

Open Access

https://doi.org/10.1155/2021/9965781

Copy DOI

Abstract

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

Highlights

With the increasing public demand for security and the development of machine vision technology, research in the field of intelligent monitoring continues to deepen, and many scholars are committed to use the powerful computing power of computers to process surveillance video data to strengthen security control
The main contributions of this paper are in the following aspects: (1) the robustness of fight behavior recognition from multiple video perspectives is considered in our proposed method
We evaluate the performance of our classifier in terms of accuracy, missing alarm (MA), false alarm (FA), and F1-score and compare the Random Forest (RF) classifier with other representative classifiers including support vector machine (SVM), adaptive enhancement (AdaBoost) and bagging guided aggregation (Bagging)

Summary

Introduction

With the increasing public demand for security and the development of machine vision technology, research in the field of intelligent monitoring continues to deepen, and many scholars are committed to use the powerful computing power of computers to process surveillance video data to strengthen security control. Security systems relying on human observers are inefficient, and it may cause missed alarms due to the limited human capability to monitor surveillance video continuously, resulting in an urgent demand to a research of automatic alarm technology for the abnormal behaviors like fighting. In the behavior analysis methods based on low-level image information, motion trajectory [6], shape features [7], texture features [8], optical flow features, and other image information are used to perform the behavior analysis.

Methods

Results

Conclusion