Machine Learning
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Feature Co-Occurrence Selection for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Cat face detection with two heterogeneous features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a fast and accurate pedestrian detection framework based on cascaded classifiers with two complementary features. Our pipeline starts with a cascade of weak classifiers using Haar-like features followed by a linear SVM classifier relying on the Co-occurrence Histograms of Oriented Gradients (CoHOG). CoHOG descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar features are computationally efficient but not highly discriminative for extremely varying texture and shape information such as pedestrians with different clothing and stances. Therefore, the combination of both classifiers enables fast and accurate pedestrian detection. Additionally, we propose reducing CoHOG descriptor dimensionality using Principle Component Analysis. The experimental results on the DaimlerChrysler benchmark dataset show that we can reach very close accuracy to the CoHOG-only classifier but in less than 1/1000 of its computational cost.