Towards Modelling High-density Crowds for Assisting Planning and Safety – MOHICANS

One of the most active recent developments in computer vision has been the analysis of crowded scenes. The interest that this specific field has raised may be explained from two different perspectives. In terms of applicability, continuous surveillance of public and sensitive areas has benefited from the advancements in hardware and infrastructure, and the bottleneck moved towards the processing level, where human supervision is a laborious task which often requires experienced operators. Other circumstances involving the analysis of dense crowds are represented by large scale events (sport events, religious or social gatherings) which are characterized by very high densities (at least locally) and an increased risk of congestions. From a scientific perspective, the detection of pedestrians in different circumstances, and furthermore the interpretation of their actions involve a wide range of branches of computer vision and machine learning.

Single camera analysis
This represents the typical setup for a broad range of applications related to prevention and detection in public and private environments. Although some camera networks may contain thousands of units, it is quite common to perform processing tasks separately in each view. However, single view analysis is limited by the field of view of individual cameras and furthermore by the spatial layout of the scene; also, frequent occlusions in crowded scenes hamper the performance of standard detection algorithms and complexify tracking.

Multiple camera analysis
Multiple camera analysis has the potential to overcome problems related to occluded scenes, long trajectory tracking or coverage of wider areas. Among the main scientific challenges, these systems require mapping different views to the same coordinate system; also, solutions for the novel problems they address (detection in dense crowds, object and track association, re-identification etc.) may not be obtained simply by employing and extending previous strategies used in single camera analysis.

In our study, we focus on solving the problem of analyzing the dynamics of a high-density crowd. The goal of the present proposal is to tackle the major challenge of detecting and tracking simultaneously as particles thousands of pedestrians forming a high-density crowd, and based on real data observations, to assist in proposing and validating a particle interaction model for crowd flow. Our project is original in its aim of performing particle level analysis, as well as through its emphasis on wide area multiple camera tracking. The strategy we intend to follow is based on a feedback loop involving
particle segmentation and tracking, which aims to address the main difficulty of this problem, the uncertainty of data association. The value of such a study rests on the need for better solutions for human urban environments and for transport infrastructures, that not only improve the efficiency of the flows involved, but also do it in such a way as to increase and not diminish the quality of life. Another important prerogative of such research is to prevent fatalities during large scale events and gatherings.

Toward the end of the project, we intend to propose a methodology for the analysis of highly-dense crowds which benefits from the recent developments in single camera tracking, and also proposes effective data association solutions among multiple cameras. Secondly, we intend to support the research community by providing a multi-camera dataset which would also allow for a stronger implication of additional fields involved in the general study of crowds, mainly physics, control, simulations and sociology.