Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

Fahed  Awad; Rufaida  Shamroukh

doi:10.4236/ijis.2014.42006

Fahed Awad^*, Rufaida Shamroukh

Abstract: This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

Keywords: Urban Search and Rescue, Human-Robot Interaction, Autonomous Systems, Intelligent Interface, Neural Networks

Cite this paper: Awad, F. and Shamroukh, R. (2014) Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks. International Journal of Intelligence Science, 4, 39-53. doi: 10.4236/ijis.2014.42006.

References

[1] Shah, B. and Choset, H. (2004) Survey on Urban Search and Rescue Robotics. Journal of the Robotics Society of Japan, 22, 582-586. http://dx.doi.org/10.7210/jrsj.22.582

[2] Moradi, S. (2002) Victim Detection with Infrared Camera in a Rescue Robot. IEEE International Conference on Artificial Intelligence Systems. http://ce.sharif.ac.ir/~rescuerobot/downloads/victim_detection.pdf

[3] Burion, S. (2004) Human Detection for Robotic Urban Search and Rescue. Infoscience Database of the Publications and Research Reports, Technical Report.

[4] Nakajimaa, C., Pontilb, M., Heiselec, B. and Poggioc, T. (2003) Full-Body Person Recognition System. The Journal of the Pattern Recognition Society, 36, 1997-2006. http://dx.doi.org/10.1016/s0031-3203(03)00061-x

[5] Mohan, A., Papageorgiou, C. and Poggio, T. (2001) Example-Based Object Detection in Images by Components. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 349-361. http://dx.doi.org/10.1109/34.917571

[6] Engle, S. and Whalen, S. (2003) Autonomous Multi-Robot Foraging in a Simulated Environment. UC Davis Report.

[7] Cavalcanti, C. and Gomes, H. (2005) People Detection in Still Images Based on a Skin Filter and Body Part Evidence. The Brazilian Symposium on Computer Graphics and Image Processing,

[8] National Institute of Standards and Technology (NIST) (2011) Performance Metrics and Test Arenas for Autonomous Mobile Robots. http://www.isd.mel.nist.gov/projects/USAR/

[9] Jacoff, A., Messina, E., Weiss, B., Tadokoro, S. and Nakagawa, Y. (2003) Test Arenas and Performance Metrics for Urban Search and Rescue Robots. Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 27-31 October 2003, 3396-3403. http://dx.doi.org/10.1109/IROS.2003.1249681

[10] Jacoff, A., Messina, E., and Evans, J. (2002) Performance Evaluation of Autonomous Mobile Robots. Industrial Robot—An International Journal, 29, 259-267.

[11] Jacoff, A., Messina, E. and Evans, J. (2001) Experiences in Deploying Test Arenas for Autonomous Mobile Robots. Proceedings of the 2001 Performance Metrics for Intelligent Systems, Mexico, 4 September 2001, 1-8.

[12] Jacoff, A., Weiss, B. and Messina, E. (2003) Evolution of a Performance Metric for Urban Search and Rescue Robots. Proceedings of the 2003 Performance Metrics for Intelligent Systems, Gaithersburg, 16-18 August 2003, 1-11.

[13] Trierscheid, M., Pellenz, J., Paulus, D. and Balthasar, D. (2008) Hyperspectral Imaging for Victim Detection with Rescue Robots. Proceedings of the 2008 IEEE International Workshop on Safety, Security and Rescue Robotics, Sendai, 21-24 October 2008, 7-12. http://dx.doi.org/10.1109/SSRR.2008.4745869

[14] Zhao, K., Wang, X., Li, Y. and Yu, X. (2006) A Life-Detection System for Special Rescuing Robots. The 9th International Conference on Control, Automation, Robotics and Vision, Singapore, 5-8 December 2006, 1-5.

[15] Stergiou, C. and Siganos, D. (2006) Neural Networks. Surveys and Presentations in Information Systems Engineering. SURPRISE 96 Journal.

[16] Russell, S. and Norving, P. (2003) Artificial Intelligence—A Modern Approach. 2nd Edition, Prentice Hall, Upper Saddle River.

[17] Gonzalez, R. and Woods, R. (2002) Digital image processing. 2nd Edition, Prentice Hall, Upper Saddle River.