Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Detection Using Depth and Gray Images
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time human detection using relational depth similarity features
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor
AIPR '11 Proceedings of the 2011 IEEE Applied Imagery Pattern Recognition Workshop
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Two-dimensional image based human detection methods have been widely used in surveillance system. However, detecting human in the presence of occlusion is still a challenge for such image based systems. In this paper, a human detection method aiming to handle occlusions by using the depth data obtained from 3D imaging methods, such as those easily acquired from the Microsoft Kinect depth sensor, is proposed. In the context of surveillance setting, background subtraction on the depth data can be used to extract foreground regions which may correspond to humans. The proposed method analyzes the 3D data of the foreground regions using a "split-merge" approach. Over-segmentation and clustering are preformed on foreground regions followed by the height validation. Experimental results demonstrate that the proposed method outperforms two state-of-art human detection methods.