Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
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
Improving object detection with boosted histograms
Image and Vision Computing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
On exploration of classifier ensemble synergism in pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
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Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.