Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking

TL;DR

This paper introduces a novel sliding-window optimization framework using an Ambiguity-Clearness Graph for multi-object tracking, effectively handling occlusions and outliers while balancing online and batch tracking advantages.

Contribution

It proposes an Approximation-Shrink Scheme with an Ambiguity-Clearness Graph and a sliding window approach, improving multi-object tracking performance and convergence.

Findings

Outperforms traditional online tracking with small windows

Approaches batch tracking performance with limited window size

Maintains sequence independence and conflict avoidance

Abstract

Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers. In order to handle this problem, we propose an Approximation-Shrink Scheme for sequential optimization. This scheme is realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and maintain sequence independent, as well as a sliding window optimization framework to constrain the size of state space and guarantee convergence. Based on this window-wise framework, the states of targets are clustered in a self-organizing manner. Moreover, we show that the traditional online and batch tracking methods can be embraced by the window-wise framework. Experiments indicate that with only a small window, the optimization performance can be much better than online methods and approach to batch methods.