A new approach for vehicle detection in congested traffic scenes based on strong shadow segmentation
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
A study on sampling strategies in space-time domain for recognition applications
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Hamming selection pruned sets (HSPS) for efficient multi-label video classification
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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In this paper we propose a framework for categorization of different types of vehicles. The difficulty comes from the high inter-class similarity and the high intra-class variability. We address this problem using a part-based recognition system. We particularly focus on the trade-off between the number of parts included in the vehicle models and the recognition rate, i.e the trade-off between fast computation and high accuracy. We propose a high-level data transformation algorithm and a feature selection scheme adapted to hierarchical SVM classifiers to improve the performance of part-based vehicle models. We have tested the proposed framework on real data acquired by infrared surveillance cameras, and on visible images too. On the infrared dataset, with the same speedup factor of 100, our accuracy is 12% better than the standard one-versus-one SVM.