Neural network based online feature selection for vehicle tracking

Authors:
Tie Liu;Nanning Zheng;Hong Cheng
Affiliations:
The Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi, China;The Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi, China;The Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

Aiming at vehicle tracking with a single moving camera for autonomous driving, this paper presents a strategy of online feature selection combined with related process framework. Detected vehicle can provide more information for tracking. A principal component analysis neural network is used to select appearance features online. Then the positive and negative histogram models using selected features are found for the detected vehicle and the surroundings. A likelihood function is defined based on histogram models, and it can be used as a simple classifier. For selected multiple features, the corresponding multiple classifiers are combined with a single layer perceptron. Experimental results indicate the validity and real-time performance.