Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

Manoj Kumar,Charul Bhatnagar

doi:10.2991/ijcis.2017.10.1.16

Abstract

In the current era, crowd behavior analysis is important topic due to the significance of video surveillance in the public area. Literature presents a handful of works for crowd behavior detection and analysis. Even though, the complicated challenges such as, low quality video, wide variation in the density of crowds and difficult motion patterns pose a complicated challenges for the researchers in crowd behavior detection. In order to alleviate these issues, we develop a crowd behavior detection system using hybrid tracking model and integrated features enabled neural network. The proposed crowd behavior detection system estimate the direction of movement of objects as well their activity using proposed GLM-based neural network. The proposed GLM-based neural network integrates the LM algorithm with genetic algorithm to improve the learning process of neural network. The performance of the proposed crowd behavior detection algorithm is validated with five different video and the performance is extensively analyzed using accuracy. From research outcome, we proved that the proposed system obtained the maximum accuracy of 95% which is higher than the existing methods taken for comparison.

Highlights

Intelligent video surveillance has been became one of thekey research area in computer vision due to heightened security concerns
Even though many research topics are available in video surveillance, tracking and behavior analysis from the crowded video are challenging problem because it has a number of applications including eventmonitoring, behavior modeling, traffic control and security applications [1,2,3,4]
The output of the tracking path for marathon 1 is given in figure 4.c

Summary

Introduction

Intelligent video surveillance has been became one of thekey research area in computer vision due to heightened security concerns. A number of security agencies specialized in dense crowd management have emerged to respond to the need. This problem has started to draw attention of the research community for automatic detection of abnormal crowd behaviors during public events [14]. Speaking, crowd behavior analysis can be divided into two tasks: (1) motion information extraction and (2) abnormal behavior modeling. The former usually amounts to crowd tracking. Higher level models of crowd behavior can be used to detect anomalous events [15]

Methods

Results

Conclusion