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- Title
PEDESTRIAN DETECTION SYSTEM USING CASCADED BOOSTING WITH INVARIANCE OF ORIENTED GRADIENTS.
- Authors
WANG, CHI-CHEN RAXLE; WU, JIN-YI; LIEN, JENN-JIER JAMES
- Abstract
This study presents a novel learning-based pedestrian detection system capable of automatically detecting individuals of different sizes and orientations against a wide variety of backgrounds, including crowds, even when the individual is partially occluded. To render the detection performance robust toward the effects of geometric and rotational variations in the original image, the feature extraction process is performed using both rectangular- and circular-type blocks of various sizes and aspect ratios. The extracted blocks are rotated in accordance with their dominant orientation(s) such that all the blocks extracted from the input images are rotationally invariant. The pixels within the cells in each block are then voted into rectangular- and circular-type 9-bin histograms of oriented gradients (HOGs) in accordance with their gradient magnitudes and corresponding multivariate Gaussian-weighted windows. Finally, four cell-based histograms are concatenated using a tri-linear interpolation technique to form one 36-dimensional normalized HOG feature vector for each block. The experimental results show that the use of the Gaussian-weighted window approach and tri-linear interpolation technique in constructing the HOG feature vectors improves the detection performance from 91% to 94.5%. In the proposed scheme, the detection process is performed using a cascaded detector structure in which the weak classifiers and corresponding weights of each stage are established using the AdaBoost self-learning algorithm. The experimental results reveal that the cascaded structure not only provides a better detection performance than many of the schemes presented in the literature, but also achieves a significant reduction in the computational time required to classify each input image.
- Subjects
DETECTORS; PHYSICS instruments; SECURITY systems; DEMODULATION; NUMERICAL analysis; ARTIFICIAL intelligence; INTERPOLATION; ELECTRONIC data processing
- Publication
International Journal of Pattern Recognition & Artificial Intelligence, 2009, Vol 23, Issue 4, p801
- ISSN
0218-0014
- Publication type
Article
- DOI
10.1142/S0218001409007363