Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Human gesture recognition system for TV viewing using time-of-flight camera
Multimedia Tools and Applications
Histogram of oriented normal vectors for object recognition with a depth sensor
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Human detection with occlusion handling by over-segmentation and clustering on foreground regions
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Many conventional human detection methods use features based on gradients, such as histograms of oriented gradients (HOG), but human occlusions and complex backgrounds make accurate human detection difficult. Furthermore, real-time processing also presents problems because the use of raster scanning while varying the window scale comes at a high computational cost. To overcome these problems, we propose a method for detecting humans by Relational Depth Similarity Features(RDSF) based on depth information obtained from a TOF camera. Our method calculates the features derived from a similarity of depth histograms that represent the relationship between two local regions. During the process of detection, by using raster scanning in a 3D space, a considerable increase in speed is achieved. In addition, we perform highly accurate classification by considering of occlusion regions. Our method achieved a detection rate of 95.3% with a false positive rate of 1.0%. It also had a 11.5% higher performance than the conventional method, and our detection system can run in real-time (10 fps).