The Multi-strand Graph for a PTZ Tracker

• NCJ Number: 253332
• Journal: Journal of Mathematical Imaging and Vision Volume: 60 Issue: 4 Dated: 2018 Pages: 594-608
• Author(s): Shachaf Melman; Yael Moses; Cerard Medioni; Yinghao Cai
• Date Published: 2018
• Length: 15 pages
• Grant Number(s): 2011-IJ-CX-K054
• Agencies: NIJ-Sponsored
• Sponsoring Agency: National Institute of Justice (NIJ)
• Publication Format: Article
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Language: English
• Country: United States of America

Annotation

Since a central component of a PTZ tracking system is a scheduling algorithm that determines which target to zoom in on, particularly when the high-resolution images are also used for tracklet matching, the current study examined this scheduling problem from a theoretical perspective, proposing 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.

Abstract

High-resolution images can be used to resolve matching ambiguities between trajectory fragments (tracklets), which is a key challenge in multiple-target tracking. A pan-tilt-zoom (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 enables tracking of many targets, while the close-up view allows individuals to be identified from high-resolution images of their faces. The current project determined that the MSG allows efficient scheduling, as well as resolving of 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 were used to evaluate the scheduling algorithm and to demonstrate its superiority over a naïve one. (publisher abstract modified)