Globally optimum multiple object tracking

Ismail Oner Sebe; Suya You; Ulrich Neumann

doi:10.1117/12.603210

19 May 2005 Globally optimum multiple object tracking

Ismail Oner Sebe, Suya You, Ulrich Neumann

Proceedings Volume 5810, Acquisition, Tracking, and Pointing XIX; (2005) https://doi.org/10.1117/12.603210
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Robust and accurate tracking of multiple objects is a key challenge in video surveillance. Tracking algorithms generally suffer from either one or more of the following problems, excluding detection errors. First, objects can be incorrectly interpreted as one of the other objects in the scene. Second, interactions between objects, such as occlusions, may cause tracking errors. Third, globally-optimum tracking is hard to achieve since the combinatorial assignment problem is NP-Complete. We present a modified Multiple-Hypothesis Tracking algorithm, MHT, for globally optimum tracking of moving objects. The system defines five states for tracked objects: appear, disappear, track, split, and merge, and these states cover all the interactions of object pairs. After the detection of objects in the current frame, a resemblance matrix is computed for every object pair. We convert the two-dimensional resemblance matrix into a three-dimensional state-likelihood structure and use a MHT technique to solve the state-assignment problem in 3D. This prevents incorrect assignments due to local minima in the assignment process. Moreover, the method models occlusion cases with the split and merge states. Finally, this method approximates a globally optimum state assignment in polynomial time complexity.

Citation Download Citation

Ismail Oner Sebe, Suya You, and Ulrich Neumann "Globally optimum multiple object tracking", Proc. SPIE 5810, Acquisition, Tracking, and Pointing XIX, (19 May 2005); https://doi.org/10.1117/12.603210

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available