Coupled data association and L1 minimization for multiple object tracking under occlusion

Xue Wang; Qing Wang

doi:10.1117/12.2073887

5 November 2014 Coupled data association and l1 minimization for multiple object tracking under occlusion

Xue Wang, Qing Wang

Proceedings Volume 9273, Optoelectronic Imaging and Multimedia Technology III; 927322 (2014) https://doi.org/10.1117/12.2073887
Event: SPIE/COS Photonics Asia, 2014, Beijing, China

Abstract

We propose a novel multiple object tracking algorithm in a particle filter framework, where the input is a set of candidate regions obtained from Robust Principle Component Analysis (RPCA) in each frame, and the goals is to recover trajectories of objects over time. Our method adapts to the changing appearance of objects, due to occlusion, illumination changes and large pose variations, by incorporating a l1 minimization-based appearance model into the Maximize A Posterior (MAP) inference. Though L1 trackers have showed impressive tracking accuracy, they are computationally demanding for multiple object tracking. Conventional data association methods using simple nonparametric appearance model, such as histogram-based descriptor, may suffer from drastic changing object appearance. The robust tracking performance of our approach has been validated with a comprehensive evaluation involving several challenging sequences and state-of-the-art multiple object trackers.

Citation Download Citation

Xue Wang and Qing Wang "Coupled data association and l1 minimization for multiple object tracking under occlusion", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 927322 (5 November 2014); https://doi.org/10.1117/12.2073887

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