Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment

To realize robust human detection in an actual office work scenario, this paper proposes two ideas using top-view depth cameras. To deal with the changing geometric human shapes caused by body posture (e.g., sitting, standing, and crouching), we propose two features to describe the human upper-back shape, i.e., roundness and size of a height-continuous region. For alleviating the influences of partial loss of depth information caused by occlusions and by the absorption of infrared light, we propose an adaptive feature adjustment algorithm, which utilizes implicitly included information in the missing region. We implemented the proposed algorithm on a system with 13 depth cameras. Application to 100-hours (10 workdays) of actual office data demonstrated that the upper-back features complement the existing head-shoulder features. It also demonstrated that both of the proposals contributed to a more robust human detection and attained 97.7 % accuracy.

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

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

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

[4]  Xiaogang Wang,et al.  Intelligent multi-camera video surveillance: A review , 2013, Pattern Recognit. Lett..

[5]  Tae-Seong Kim,et al.  Daily Human Activity Recognition Using Depth Silhouettes and R\mathcal{R} Transformation for Smart Home , 2011, ICOST.

[6]  Huadong Ma,et al.  Scene-adaptive accurate and fast vertical crowd counting via joint using depth and color information , 2013, Multimedia Tools and Applications.

[7]  Xia Liu,et al.  Pedestrian detection using stereo night vision , 2004, IEEE Transactions on Vehicular Technology.

[8]  Pamela J. Hinds,et al.  What Do We Know about Proximity and Distance in Work Groups? A Legacy of Research , 2002 .

[9]  Anton Kummert,et al.  Applications for a people detection and tracking algorithm using a time-of-flight camera , 2014, Multimedia Tools and Applications.

[10]  Rainer Stiefelhagen,et al.  Sleep position classification from a depth camera using Bed Aligned Maps , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[11]  Qixiang Ye,et al.  Real-Time Multipedestrian Tracking in Traffic Scenes via an RGB-D-Based Layered Graph Model , 2015, IEEE Transactions on Intelligent Transportation Systems.

[12]  G. Nagarajan,et al.  Improving ATM security via face recognition , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[13]  Giuseppe Valenzise,et al.  Local visual features extraction from texture+depth content based on depth image analysis , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[14]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[15]  Ahmad Jalal,et al.  Dense depth maps-based human pose tracking and recognition in dynamic scenes using ridge data , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[16]  Tae-Seong Kim,et al.  Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home , 2012, IEEE Transactions on Consumer Electronics.

[17]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

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

[19]  Daijin Kim,et al.  Shape and Motion Features Approach for Activity Tracking and Recognition from Kinect Video Camera , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops.

[20]  Jun Cheng,et al.  Human contour extraction from RGBD camera for action recognition , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[21]  Pascal Fua,et al.  Probability occupancy maps for occluded depth images , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Majid Mirmehdi,et al.  Multiple Human Tracking in RGB-D Data: A Survey , 2016, ArXiv.

[23]  Matteo Munaro,et al.  Tracking people within groups with RGB-D data , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Dariu Gavrila,et al.  Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[25]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[26]  Majid Mirmehdi,et al.  Multiple human tracking in RGB-depth data: a survey , 2017, IET Comput. Vis..

[27]  Haibin Cai,et al.  An adaptive wireless passive human detection via fine-grained physical layer information , 2016, Ad Hoc Networks.

[28]  Nicholas R. Gans,et al.  A Multi-view camera-projector system for object detection and robot-human feedback , 2013, 2013 IEEE International Conference on Robotics and Automation.

[29]  Jake K. Aggarwal,et al.  Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Marco Morana,et al.  Human Activity Recognition Process Using 3-D Posture Data , 2015, IEEE Transactions on Human-Machine Systems.

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

[32]  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).

[33]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Daijin Kim,et al.  Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments , 2016, J. Comput. Networks Commun..

[36]  Tae-Seong Kim,et al.  Human Activity Recognition via Recognized Body Parts of Human Depth Silhouettes for Residents Monitoring Services at Smart Home , 2013 .

[37]  Carrie R. Leana,et al.  Organizational Social Capital and Employment Practices , 1999 .

[38]  C. Fombrun,et al.  Social Network Analysis For Organizations , 1979 .

[39]  Silvio Savarese,et al.  Detecting and tracking people using an RGB-D camera via multiple detector fusion , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[40]  Alex Pentland,et al.  Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  Hironobu Fujiyoshi,et al.  Human detection by Haar-like filtering using depth information , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[42]  Daijin Kim,et al.  A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments , 2014, Sensors.

[43]  James J. Little,et al.  Learning to Track and Identify Players from Broadcast Sports Videos , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[45]  Arnoldo Díaz-Ramírez,et al.  Human Detection and Tracking in Healthcare Applications Through the Use of a Network of Sensors , 2014, Human Behavior Understanding in Networked Sensing.

[46]  Ying Wu,et al.  Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[47]  Sung-Jea Ko,et al.  Robust people counting system based on sensor fusion , 2012, IEEE Transactions on Consumer Electronics.