People Detection and Tracking from an RGB-D Camera in Top-View Configuration: Review of Challenges and Applications

This paper presents a literature review on the use of RGB-D camera for people detection and tracking. Our aim is to use this state-of-the-art report to demonstrate the potential of top-view configuration for people detection and tracking applications in several sub-domains, to outline key limitations and to indicate areas of technology, where solutions for remaining challenges may be found. The survey examines the success of RGB-D cameras because of their affordability and for the additional rough depth information coupled with visual images that provide. These cameras in configuration top-view have already been successfully applied in the several fields to univocally identify people and to analyse behaviours and interactions. From this report, it emerges that detecting and tracking people can be a valuable source of information for many fields and purposes.

[1]  Emanuele Frontoni,et al.  HMM-based Activity Recognition with a Ceiling RGB-D Camera , 2017, ICPRAM.

[2]  Javier Lorenzo-Navarro,et al.  An Study on Re-identification in RGB-D Imagery , 2012, IWAAL.

[3]  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).

[4]  Bogdan Kwolek,et al.  Fall detection using ceiling-mounted 3D depth camera , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[5]  Emanuele Frontoni,et al.  Pervasive System for Consumer Behaviour Analysis in Retail Environments , 2016, VAAM/FFER@ICPR.

[6]  Saul Greenberg,et al.  Cross-device interaction via micro-mobility and f-formations , 2012, UIST.

[7]  Emanuele Frontoni,et al.  Shopper Analytics: A Customer Activity Recognition System Using a Distributed RGB-D Camera Network , 2014, VAAM@ICPR.

[8]  Fakhreddine Ababsa,et al.  3D Human Tracking in a Top View Using Depth Information Recorded by the Xtion Pro-Live Camera , 2013, ISVC.

[9]  Frank Dittrich,et al.  Pixelwise object class segmentation based on synthetic data using an optimized training strategy , 2014, 2014 First International Conference on Networks & Soft Computing (ICNSC2014).

[10]  Fakhreddine Ababsa,et al.  Hybrid 3D–2D human tracking in a top view , 2014, Journal of Real-Time Image Processing.

[11]  Yun Wei,et al.  Human Detection using HOG Features of Head and Shoulder Based on Depth Map , 2013, J. Softw..

[12]  Metin Ozkan,et al.  People counting system by using kinect sensor , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[13]  Michael Rauter Reliable Human Detection and Tracking in Top-View Depth Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[14]  Louahdi Khoudour,et al.  A People Counting System Based on Dense and Close Stereovision , 2008, ICISP.

[15]  Filip Malawski Top-view people counting in public transportation using Kinect , 2014 .

[16]  Pradit Mittrapiyanuruk,et al.  Field Seeding Algorithm for People Counting Using KINECT Depth Image , 2016 .

[17]  Emanuele Frontoni,et al.  Person Re-identification Dataset with RGB-D Camera in a Top-View Configuration , 2016, VAAM/FFER@ICPR.

[18]  Primo Zingaretti,et al.  An automatic analysis of shoppers behaviour using a distributed RGB-D cameras system , 2014, 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[19]  Michifumi Yoshioka,et al.  Investigation of Customer Behavior Analysis Based on Top-View Depth Camera , 2017, 2017 IEEE Winter Applications of Computer Vision Workshops (WACVW).

[20]  Kazutaka Shimada,et al.  Person Identification Using Top-View Image with Depth Information , 2012, SNPD.

[21]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

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

[23]  Bogdan Kwolek,et al.  Detecting human falls with 3-axis accelerometer and depth sensor , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Samir Bouaziz,et al.  3D-sensing Distributed Embedded System for People Tracking and Counting , 2015, 2015 International Conference on Computational Science and Computational Intelligence (CSCI).

[25]  Ennio Gambi,et al.  A Depth-Based Fall Detection System Using a Kinect® Sensor , 2014, Sensors.

[26]  Leonidas J. Guibas,et al.  Multi-person tracking from sparse 3D trajectories in a camera sensor network , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[27]  Jordi Vitrià,et al.  A cluster-based strategy for active learning of RGB-D object detectors , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[28]  Li-Chen Fu,et al.  Representative Body Points on Top-View Depth Sequences for Daily Activity Recognition , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[29]  Mario Vento,et al.  Counting people by RGB or depth overhead cameras , 2016, Pattern Recognit. Lett..

[30]  J. Lorenzo,et al.  People counting with re-identification using depth cameras , 2011, ICDP.

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  Pedro F. Felzenszwalb Learning models for object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[33]  David Herman,et al.  Human gesture recognition using top view depth data obtained from Kinect sensor , 2015 .

[34]  Junjie Yan,et al.  Water Filling: Unsupervised People Counting via Vertical Kinect Sensor , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[35]  Emanuele Frontoni,et al.  Human activity analysis for in-home fall risk assessment , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[36]  Luigi di Stefano,et al.  People Tracking Using a Time-of-Flight Depth Sensor , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[37]  Emanuele Frontoni,et al.  Advanced integration of multimedia assistive technologies: A prospective outlook , 2014, 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[38]  Ting-En Tseng,et al.  Real-time people detection and tracking for indoor surveillance using multiple top-view depth cameras , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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