Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Munir, Sirajum; Arora, Ripudaman Singh; Hesling, Craig; Li, Juncheng; Francis, Jonathan; Shelton, Charles; Martin, Christopher; Rowe, Anthony; Berges, Mario

doi:10.1109/rtas.2017.8

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Conference · Tue Apr 18 00:00:00 EDT 2017 · 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

DOI:https://doi.org/10.1109/rtas.2017.8· OSTI ID:1811682

Munir, Sirajum ^[1]; Arora, Ripudaman Singh; Hesling, Craig; Li, Juncheng; Francis, Jonathan; Shelton, Charles; Martin, Christopher; Rowe, Anthony; Berges, Mario

Robert Bosch LLC

Occupancy estimation is an important primitive for a wide range of applications including building energy efficiency, safety, and security. In this paper, we explore the potential of using depth sensors to detect, estimate, identify, and track occupants in buildings. While depth sensors have been widely used for human detection and gesture recognition, computer vision algorithms are typically run on a powerful computer like XBOX or Intel R CoreTM i7 processor. In this work, we develop a prototype system called FORK using off-the-shelf components that performs the entire depth data processing on a cheaper and low power ARM processor in real-time. As ARM processors are extremely weak in running computer vision algorithms, FORK is designed to detect humans and track them in a very efficient way by leveraging a novel lightweight model based approach instead of traditional approaches based on histogram of oriented gradients (HOG) features. Unlike other camera based approaches, FORK is much less privacy invasive (even if the sensor is compromised). Based on a complete implementation, real-world deployment, and extensive evaluation at realistic scenarios, we observe that FORK achieves over 99% accuracy in real-time (4-9 FPS) in occupancy estimation.

View Conference

Research Organization:: Robert Bosch LLC

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

DOE Contract Number:: EE0007682

OSTI ID:: 1811682

Journal Information:: 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), Journal Name: 2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

Country of Publication:: United States

Language:: English

References (19)

A novel method for tracking and counting pedestrians in real-time using a single camera Masoud, O.; Papanikolopoulos, N. P. IEEE Transactions on Vehicular Technology, Vol. 50, Issue 5 https://doi.org/10.1109/25.950328	journal	January 2001
A People Counting System Based on Face Detection and Tracking in a Video Zhao, Xi; Delleandrea, Emmanuel; Chen, Liming 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance https://doi.org/10.1109/AVSS.2009.45	conference	September 2009
Water Filling: Unsupervised People Counting via Vertical Kinect Sensor Zhang, Xucong; Yan, Junjie; Feng, Shikun 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance https://doi.org/10.1109/AVSS.2012.82	conference	September 2012
Real-time people counting from depth imagery of crowded environments Bondi, Enrico; Seidenari, Lorenzo; Bagdanov, Andrew D. 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) https://doi.org/10.1109/AVSS.2014.6918691	conference	August 2014
Histograms of Oriented Gradients for Human Detection Dalal, N.; Triggs, B. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) https://doi.org/10.1109/CVPR.2005.177	conference	January 2005
Human detection using depth information by Kinect Xia, Lu; Chen, Chia-Chih; Aggarwal, J. K. CVPR 2011 WORKSHOPS https://doi.org/10.1109/CVPRW.2011.5981811	conference	June 2011
Counting people in crowds with a real-time network of simple image sensors No authors listed Proceedings Ninth IEEE International Conference on Computer Vision, Vol. 1, p. 122-129 https://doi.org/10.1109/ICCV.2003.1238325	conference	December 2002
Tracking and counting moving people Rossi, M.; Bozzoli, A. Proceedings of 1st International Conference on Image Processing https://doi.org/10.1109/ICIP.1994.413857	conference	November 1994
A camera-based system for tracking people in real time Segen, J. Proceedings of 13th International Conference on Pattern Recognition https://doi.org/10.1109/ICPR.1996.546795	conference	January 1996
Tracking human motion using multiple cameras Cai, Q.; Aggarwal, J. K. Proceedings of 13th International Conference on Pattern Recognition https://doi.org/10.1109/ICPR.1996.546796	conference	January 1996
A Kinect-based people-flow counting system Hsieh, Ching-Tang; Wang, Hui-Chun; Wu, Yeh-Kuang 2012 International Symposium on Intelligent Signal Processing and Communications Systems https://doi.org/10.1109/ISPACS.2012.6473470	conference	November 2012
Smart cameras as embedded systems Wolf, W.; Ozer, B.; Lv, T. Computer, Vol. 35, Issue 9 https://doi.org/10.1109/MC.2002.1033027	journal	September 2002
An efficient method for contour tracking using active shape models Baumberg, A. M.; Hogg, D. C. Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects https://doi.org/10.1109/MNRAO.1994.346236	conference	November 1994
Real-Time People Counting Using Multiple Lines Barandiaran, Javier; Murguia, Berta; Boto, Fernando 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services https://doi.org/10.1109/WIAMIS.2008.27	conference	January 2008
Doorjamb Hnat, Timothy W.; Griffiths, Erin; Dawson, Ray Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems https://doi.org/10.1145/2426656.2426687	conference	November 2012
Occupancy estimation using ultrasonic chirps Shih, Oliver; Rowe, Anthony Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems https://doi.org/10.1145/2735960.2735969	conference	April 2015
PIR sensors Narayana, Sujay; Prasad, R. Venkatesha; Rao, Vijay S. Proceedings of the 14th International Conference on Information Processing in Sensor Networks https://doi.org/10.1145/2737095.2742561	conference	April 2015
Sensor Andrew: Large-scale campus-wide sensing and actuation Rowe, A.; Berges, M. E.; Bhatia, G. IBM Journal of Research and Development, Vol. 55, Issue 1.2 https://doi.org/10.1147/JRD.2010.2089662	journal	January 2011
Human Detection using HOG Features of Head and Shoulder Based on Depth Map Tian, Qing; Zhou, Bo; Zhao, Wen-hua Journal of Software, Vol. 8, Issue 9 https://doi.org/10.4304/jsw.8.9.2223-2230	journal	September 2013

Similar Records

FORK: fine grained occupancy estimatoR using kinect on ARM embedded platforms

Conference · Tue Nov 07 23:00:00 EST 2017 · OSTI ID:1811678

Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor

Conference · Sat Nov 09 23:00:00 EST 2019 · DATA'19: Proceedings of the 2nd Workshop on Data Acquisition To Analysis · OSTI ID:1577344

Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor

Dataset · Sat Nov 09 23:00:00 EST 2019 · OSTI ID:1576967

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
Depth sensor
Occupancy estimation

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Citation Formats

References (19)

Similar Records

Related Subjects