Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 17, Issue 4, Pages 1062-1071Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2015.2495342
Keywords
Feature selection; partial least squares; human detection
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Funding
- National Natural Science Foundation of China [61472334, 61170179, 61571379]
- Fundamental Research Funds for the Central Universities [20720130720]
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Channel feature detectors have shown great advantages in human detection. However, a large pool of channel features extracted for human detection usually contains many redundant and irrelevant features. To address this issue, we propose a robust discriminative weighted sparse partial least square approach for feature selection and apply it to human detection. Unlike partial least squares (PLS), which is a straightforward dimensionality reduction technique, we propose using sparse PLS to achieve feature selection. Furthermore, in order to obtain a robust latent matrix, we formulate a discriminative regularized weighted least square problem, where a discriminative term is incorporated to effectively distinguish positive samples from negative samples. A robust sparse weight matrix is trained based on the latent matrix and used for feature selection. Finally, we use the selected channel features to train the boosted decision trees and incorporate the weights of selected features with each tree. The human detector trained by the selected features can preserve high robustness and discriminativeness. Experimental results on some challenging human data sets demonstrate that the proposed approach is effective and achieves state-of-the-art performance.
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