Human detection in depth images via two steps

Reliable human detection is important for a wide range of applications. In this paper, a particular designed method for real-time human detection has been developed. The method is robustly in cluttered and dynamic environments, and deals with depth images. The method has two steps, first the plausible candidate positions are localized by a super-pixel based segmentation and merging approach. Then we utilize a descriptor encoding the joint of depth difference information and 3D geometric characteristics of human upper body to refine the candidates by a deep randomized decision forest classifier. Our approach, which detects human in depth images, allows very fast speed and high accuracy in three publicly available datasets.

[1]  Luc Van Gool,et al.  Random Forests for Real Time 3D Face Analysis , 2012, International Journal of Computer Vision.

[2]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Jun Liu,et al.  Reliably detecting humans with RGB-D camera with physical blob detector followed by learning-based filtering , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Bastian Leibe,et al.  Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Manuela M. Veloso,et al.  Fast human detection for indoor mobile robots using depth images , 2013, 2013 IEEE International Conference on Robotics and Automation.

[6]  Guangming Shi,et al.  Structure guided fusion for depth map inpainting , 2013, Pattern Recognit. Lett..

[7]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Kai Oliver Arras,et al.  People detection in RGB-D data , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Yu Bai,et al.  Efficient structure-preserving superpixel segmentation based on minimum spanning tree , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[12]  James M. Keller,et al.  Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor , 2012, ACCV.

[13]  Hironobu Fujiyoshi,et al.  Real-Time Human Detection Using Relational Depth Similarity Features , 2010, ACCV.

[14]  Stefano Soatto,et al.  Class segmentation and object localization with superpixel neighborhoods , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Silvio Savarese,et al.  Ieee Transaction on Pattern Analysis and Machine Intelligence 1 a General Framework for Tracking Multiple People from a Moving Camera , 2022 .

[16]  Jake K. Aggarwal,et al.  Human detection using depth information by Kinect , 2011, CVPR 2011 WORKSHOPS.

[17]  Bernt Schiele,et al.  Monocular 3D scene understanding with explicit occlusion reasoning , 2011, CVPR 2011.

[18]  Jitendra Malik,et al.  Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Ye Liu,et al.  Detecting and tracking people in real time with RGB-D camera , 2015, Pattern Recognit. Lett..

[20]  Zicheng Liu,et al.  HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Ye Liu,et al.  An ultra-fast human detection method for color-depth camera , 2015, J. Vis. Commun. Image Represent..

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.