Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets

TL;DR

This paper introduces a hierarchical approach for multi-target tracking across multiple non-overlapping cameras, utilizing constrained dominant sets clustering to unify within- and across-camera tracking, and proposes a fast, scalable algorithm based on evolutionary game dynamics.

Contribution

A novel three-layer hierarchical framework that integrates within- and across-camera tracking using constrained dominant sets clustering and a scalable evolutionary game theory algorithm.

Findings

Effective linking of broken tracks across cameras

Unified framework improves tracking accuracy

Algorithm is efficient and scalable to large datasets

Abstract

In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications.