The Multi-Strand Graph for a PTZ Tracker

TL;DR

This paper introduces the Multi-Strand Tracking Graph (MSG), a novel data structure for PTZ camera tracking that improves scheduling efficiency and ambiguity resolution in multi-target tracking scenarios.

Contribution

The paper presents the MSG, a new data structure that enhances tracklet association and scheduling efficiency in PTZ tracking systems from a theoretical perspective.

Findings

MSG outperforms naive scheduling algorithms in simulations.

The auxiliary data in MSG enables efficient ambiguity resolution.

Synthetic data experiments demonstrate the effectiveness of the proposed method.

Abstract

High-resolution images can be used to resolve matching ambiguities between trajectory fragments (tracklets), which is one of the main challenges in multiple target tracking. A PTZ camera, which can pan, tilt and zoom, is a powerful and efficient tool that offers both close-up views and wide area coverage on demand. The wide-area view makes it possible to track many targets while the close-up view allows individuals to be identified from high-resolution images of their faces. A central component of a PTZ tracking system is a scheduling algorithm that determines which target to zoom in on. In this paper we study this scheduling problem from a theoretical perspective, where the high resolution images are also used for tracklet matching. We propose a novel data structure, the Multi-Strand Tracking Graph (MSG), which represents the set of tracklets computed by a tracker and the possible associations between them. The MSG allows efficient scheduling as well as resolving -- directly or by elimination -- matching ambiguities between tracklets. The main feature of the MSG is the auxiliary data saved in each vertex, which allows efficient computation while avoiding time-consuming graph traversal. Synthetic data simulations are used to evaluate our scheduling algorithm and to demonstrate its superiority over a na\"ive one.