Transfer Learning of Motion Patterns in Traffic Scene via Convex Optimization

This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic p...

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Bibliographic Details
Main Authors: YoungJoon Yoo, Hawook Jeong, Soo Wan Kim, Jin Young Choi
Format: Conference Proceeding
Language:English
Subjects:
Linear programming
Mathematical model
Support vector machines
Surveillance
Trajectory
Vectors
Videos
Online Access:Request full text
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Summary:This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic patterns. Then, we use support vector machine (SVM) to learn the feature descriptors of traffic trajectories. The transfer learning scheme is formulated by a convex optimization problem using the geometric relation between target and source patterns. Not only parameters of SVM but also the geometric relation are found at the same time through two step minimization process of the optimization problem. Through experiments on various surveillance videos, the proposed formulation is shown to be valid by investigating the improvement of performance compared to a transfer scheme without the proposed geometric relation as well as SVM without transfer scheme.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.713