Tracking in Crowd is Challenging: Analyzing Crowd based on Physical Characteristics

TL;DR

This paper presents a novel approach for tracking individuals in crowded environments by partitioning frames, extracting motion patterns using Gaussian models of optical flow, to improve abnormal behavior detection and crowd analysis.

Contribution

It introduces a new method that partitions frames, uses KLT corners and optical flow, and models motion patterns with Gaussian distributions for better crowd tracking.

Findings

Effective in high-density crowds

Improves tracking accuracy under occlusion

Facilitates abnormal behavior detection

Abstract

Currently, the safety of people has become a very important problem in different places including subway station, universities, colleges, airport, shopping mall and square, city squares. Therefore, considering intelligence event detection systems is more and urgently required. The event detection method is developed to identify abnormal behavior intelligently, so public can take action as soon as possible to prevent unwanted activities. The problem is very challenging due to high crowd density in different areas. One of these issues is occlusion due to which individual tracking and analysis becomes impossible as shown in Fig. 1. Secondly, more challenging is the proper representation of individual behavior in the crowd. We consider a novel method to deal with these challenges. Considering the challenge of tracking, we partition complete frame into smaller patches, and extract motion pattern to demonstrate the motion in each individual patch. For this purpose, our work takes into account KLT corners as consolidated features to describe moving regions and track these features by considering optical flow method. To embed motion patterns, we develop and consider the distribution of all motion information in a patch as Gaussian distribution, and formulate parameters of Gaussian model as our motion pattern descriptor.