By Topic

Recognition of People Reoccurrences Using Bag-Of-Features Representation and Support Vector Machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kun Liu ; Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai, China ; Jie Yang

In multi-camera surveillance systems, it is important to track the same person across multiple cameras. It is also desirable to recognize the individuals who have been previously observed in a single-camera system. The method that represents a object image using a bag of visual words has been commonly used in image retrieval applications. For recognizing people, it can outperform the methods mainly based on global appearance like color histogram, and fit better to low-quality images compared to biometric features such as face and gait. In this paper we study the details in feature extraction, vocabulary building and classifier learning of the bag-of-features approach for classifying tracks of different individuals. Based on this approach, we design a online system applying incremental support vector machine learning with a decision scheme to distinguish reoccurrences from new targets. We get promising results from the evaluation with more than 100 tracks of 50 different people.

Published in:

Pattern Recognition, 2009. CCPR 2009. Chinese Conference on

Date of Conference:

4-6 Nov. 2009