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Paper details
Number 1 - March 2022
Volume 32 - 2022
A data association model for analysis of crowd structure
M. Sami Zitouni, Andrzej Śluzek
Abstract
The paper discusses a non-deterministic model for data association tasks in visual surveillance of crowds. Using detection
and tracking of crowd components (i.e., individuals and groups) as baseline tools, we propose a simple algebraic framework
for maintaining data association (continuity of labels assigned to crowd components) between subsequent video-frames in
spite of possible disruptions and inaccuracies in tracking/detection algorithms. Formally, two alternative schemes (which,
in practice, can be jointly used) are introduced, depending on whether individuals or groups can be prospectively better
tracked in the current scenario. In the first scheme, only individuals are tracked, and the continuity of group labels is inferred
without explicitly tracking the groups. In the second scheme, only group tracking is performed, and associations between
individuals are inferred from group tracking. The associations are built upon non-deterministic estimates of memberships
(individuals in groups) and estimates obtained directly from the baseline detection and tracking algorithms. The framework
can incorporate any detectors and trackers (both classical or DL-based) as long as they can provide some geometric outlines
(e.g., bounding boxes) of the crowd components. The formal analysis is supported by experiments in sample scenarios,
where the framework provides meaningful performance improvements in various crowd analysis tasks.
Keywords
data association, visual surveillance, crowd analysis, algebraic model