Multi-shot person re-identification approach based key frame selection

8 December 2015 Multi-shot person re-identification approach based key frame selection

Yousra Hadj Hassen, Walid Ayedi, Tarek Ouni, Mohamed Jallouli

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751H (2015) https://doi.org/10.1117/12.2228608
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

This paper presents a novel approach to solve the problem of person re-identification in non-overlapping camera views. We propose an appearance based method for person re-identification that condenses a set of frames of the same individual into the multi-class classifier SVM (Support Vector Machine). Still, the choice of different and most expressive frames for each target is very challenging. Besides, efficient person re-identification algorithms are computationally expensive due to the big amount of data used. One of the originalities of our method is how to select different shots during person tracking within each camera to guaranty efficient person re-identification. We evaluate our approach on the publicly available PRID 2011 multi-shot re-identification dataset and demonstrate some performance in comparison with the elimination of the proposed key frames selection.

Citation Download Citation

Yousra Hadj Hassen, Walid Ayedi, Tarek Ouni, and Mohamed Jallouli "Multi-shot person re-identification approach based key frame selection", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751H (8 December 2015); https://doi.org/10.1117/12.2228608

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