This document summarizes a method for person re-identification across non-overlapping cameras without person label information in the target domain. It proposes an Adaptive Ranking Support Vector Machines (AdaRSVM) method to address this problem. AdaRSVM relaxes the discriminative constraint to only rely on the positive mean in the target domain without matched image pairs. This domain adaptation approach achieves better re-identification performance than existing methods and can be used for applications like target position estimation, investigations, and traffic management in large camera networks.

1. A Person Cross Domain Re
identification Based Ad RSVMs

2. ABSTRACT
• This project addresses a new person re identification
problem without label information of persons under non
overlapping target cameras.
• In this propose an adaptive ranking support vector
machines (AdaRSVMs) method for re identification
under target domain cameras without person labels.
• To overcome the problems introduced due to the absence
of matched (positive) image pairs in the target domain,
we relax the discriminative constraint to a necessary
condition only relying on the positive mean in the target
domain.

3. INTRODUCTION
• In Recent years, person re-identification across
a camera network comprising multiple cameras
with non-overlapping views has become an
active research topic due to its importance in
many camera-network-based computer vision
applications.

4. Person Re-identification
• Person re identification is a feature
representation of the person image is less
sensitive to large inter-camera variations,
many re-identification methods focus on
extracting robust features.

5. Support Vector Machine(SVM)
• Classification of images can also be performed
using SVMs.
• Experimental results show that SVMs achieve
significantly higher search accuracy than
traditional query refinement schemes after just
three to four rounds of relevance feedback.
• This is also true of image segmentation
systems, including those using a modified
version SVM.

6. Existing system
• Existing schemes mainly focus On developing
either robust feature representations or
discriminative earning models.
• For the discriminative Learning methods, it is
generally assumed that the Label information of
persons is available for training.
• With the Person labels, matched (positive) and
unmatched (negative) Image pairs are generated
to train the is criminative distance model.

7. Disadvantages
• Existing system used in only small scale
networks
• This renders existing approaches inapplicable,
since the person labels are not available.

8. Proposed system
• In this paper, we propose a novel Adaptive
Ranking Support Vector Machines
(AdaRSVM) method to deal with the problem
that label information of persons is not
available under target cameras.

9. BLOCK DAIGRAM

10. Domain Adaptation
• The main objective of domain adaptation
approach is to adapt the classification model
learnt from the source domain to target domain
without serious deterioration of recognition
performance.

11. Advantages
• Our method achieves better re identification
performance than existing domain adaptation
methods derived under equal conditional
probability assumption.
• It is used in large scale camera Networks

12. Applications
• To estimate target position of object.
• Crowded areas
• Investigations
• Marketing research
• Tourists flow estimation
• Traffic management

13. Software Requirements
• Operating system : Windows XP/7.
• Coding Language : MATLAB
• Tool : MATLABR 2013a