Abstract
There are two significant challenges to standard approaches to detect humans through computer vision. First, scenarios when the poses and postures of the humans are completely unpredictable. Second, situations when there are many occlusions, i.e., only parts of the body are visible. Here a novel approach to perception is presented where a complete 3D scene model is learned on the fly to represent a 2D snapshot. In doing so, an evolutionary algorithm generates pieces of 3D code that are rendered and the resulting images are compared to the current camera picture via an image similarity function. Based on the feedback of this fitness function, a crude but very fast online evolution generates an approximate 3D model of the environment where non-human objects are represented by boxes. The key point is that 3D models of humans are available as code sniplets to the EA, which can use them to represent human shapes or portions of them if they are in the image. Results from experiments with real world data from a search and rescue application using a thermal camera are presented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Abouaf, J.: Trial by fire: teleoperated robot targets chernobyl. Computer Graphics and Applications 18(4), 10–14 (1998)
Aoyama, H., Himoto, A., Fuchiwaki, O., Misaki, D., Sumrall, T.: Micro hopping robot with ir sensor for disaster survivor detection. In: SSRR. IEEE International Workshop on Safety, Security and Rescue Robotics, pp. 189–194. IEEE Computer Society Press, Los Alamitos (2005)
Agarwal, A., Triggs, B.: Learning to track 3d human motion from silhouettes. In: Twenty-first international conference on Machine learning, ACM Press, New York (2004)
Birk, A., Carpin, S.: Rescue robotics - a crucial milestone on the road to autonomous systems. Advanced Robotics Journal 20(5), 595–695 (2006)
Bahadori, S., Iocchi, L., Nardi, D., Settembre, G.P.: Stereo vision based human body detection from a localized mobile robot. In: IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 499–504. IEEE Computer Society Press, Los Alamitos (2005)
Birk, A.: Learning geometric concepts with an evolutionary algorithm. In: Proc. of The Fifth Annual Conference on Evolutionary Programming, The MIT Press, Cambridge (1996)
Birk, A., Paul, W.J.: Schemas and genetic programming. In: Intern. Conf. on the Integration of Elementary Functions into Complex Behavior (1994)
Birk, A., Markov, S., Delchev, I., Pathak, K.: Autonomous rescue operations on the iub rugbot. In: SSRR. IEEE International Workshop on Safety, Security, and Rescue Robotics, IEEE Press, Los Alamitos (2006)
Birk, A., Pathak, K., Schwertfeger, S., Chonnaparamutt, W.: The iub rugbot: an intelligent, rugged mobile robot for search and rescue operations. In: SSRR. IEEE International Workshop on Safety, Security, and Rescue Robotics, IEEE Press, Los Alamitos (2006)
Burion, S.: Human detection for robotic urban search and rescue masters thesis. Technical report, Institute de Production Robotique (IPR) (1998)
Cucchiara, R., Grana, C., Prati, A., Vezzani, R.: Computer vision system for in-house video surveillance. Vision, Image and Signal Processing, IEE Proceedings- 152(2), 242–249 (2005)
Davids, A.: Urban search and rescue robots: from tragedy to technology. Intelligent Systems 17(2), 81–83 (2002)
Hao, Q., Brady, D.J., Guenther, B.D., Burchett, J., Shankar, M., Feller, S.: Human tracking with wireless distributed pyroelectric sensors. IEEE Sensors Journal 6(6), 1683–1696 (2006)
Hogg, D.: Model-based vision: A program to see a walking person. Image and Vision Computing 1(1), 5–20 (1983)
Jacoff, A., Messina, E., Weiss, B., Tadokoro, S., Nakagawa, Y.: Test arenas and performance metrics for urban search and rescue robots. In: IROS. Proceedings of the Intelligent and Robotic Systems Conference (2003)
Jacoff, A., Weiss, B., Messina, E.: Evolution of a performance metric for urban search and rescue. In: PERMIS. Performance Metrics for Intelligent Systems, Gaithersburg, MD (2003)
Kakadiaris, L., Metaxas, D.: Model-based estimation of 3d human motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1453–1459 (2000)
Kitano, H., Tadokoro, S.: Robocup rescue. a grand challenge for multiagent and intelligent systems. AI Magazine 22(1), 39–52 (2001)
Murphy, R., Casper, J., Micire, M.: Potential tasks and research issues for mobile robots in robocup rescue. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 339–334. Springer, Heidelberg (2001)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. Pattern Analysis and Machine Intelligence, IEEE Transactions on 23(4), 349–361 (2001)
Murphy, R.R.: Trial by fire. IEEE Robotics and Automation Magazine 11(3), 50–61 (2004)
Oren, M., Papageorgiou, C., Sinhaand, P., Osuna, E., Poggio, T.: Pedestrian detection using wavelet templates. In: Proc. Computer Vision and Pattern Recognition, June 1997, pp. 193–199 (1997)
Park, S.H., Jung, K., Hea, J.K., Kim, H.J.: Vision-based traffic surveillance system on the internet. In: Computational Intelligence and Multimedia Applications. In: ICCIMA. Third International Conference on, pp. 201–205 (1999)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int’l J. Computer Vision 38(1), 15–33 (2000)
Paul, W.J., Solomonoff, R.: Autonomous theory building systems. Annals of Operations Research 55(1), 179–193 (1995)
Paletta, L., Wiesenhofer, S., Brandle, N., Sidla, O., Lypetskyy, Y.: Visual surveillance system for monitoring of passenger flows at public transportation junctions. Intelligent Transportation Systems, 862–867 (2005)
Shah, B., Choset, H.: Survey on urban search and rescue robots. Journal of the Robotics Society of Japan (JRSJ) 22(5), 40–44 (2004)
Forrest, S.: Genetic algorithms - principles of natural selection applied to computation. Science 261, 872–878 (1993)
Sinha, P.: Object recognition via image invariants: A case study. Investigative Ophthalmology and Visual Science 35, 1735–1740 (1994)
Snyder, R.G.: Robots assist in search and rescue efforts at wtc. IEEE Robotics and Automation Magazine 8(4), 26–28 (2001)
Sparacino, F.: In: Inter-face body boundaries, issue editor emanuele quinz, anomalie, n.2, paris, france, anomos (2001), http://citeseer.ist.psu.edu/615750.html
Scholtz, J., Young, J., Drury, J., Yanco, H.: Evaluation of human-robot interaction awareness in search and rescue. In: Proceedings of the International Conference on Robotics and Automation, ICRA’2004, pp. 2327–2332. IEEE Press, Los Alamitos (2004)
Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Rosenberg, C., Roy, N., Schulte, J., Schulz, D.: Minerva: A second-generation museum tour-guide robot. In: Proceedings of the International Conference on Robotics and Automation (ICRA) (1999)
Teo, A.W., Garg, H.K., Puthusserypady, S.: Detection of humans buried in rubble: an electronic nose to detect human body odor. In: Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, EMBS/BMES, vol. 3, pp. 1811–1812 (2002)
Valentine, D., Abdi, H., O’Toole, A.J., Cottrell, G.W.: Connectionist models of face processing: a survey. Pattern Recognition 27 (1994)
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Yow, K., Cipolla, R.: Feature-based human face detection. Image and Vision Computing 15(9), 713–735 (1997)
Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. Pattern Analysis and Machine Intelligence, IEEE Transactions on 24(1), 34–58 (2002)
Yang, J., Stiefelhagen, R., Meier, U., Waibel, A.: Real-time face and facial feature tracking and applications. In: Proceedings of Auditory-Visual Speech Processing Conference, pp. 79–84. Terrigal, South Wales, Australia (1998)
Yuille, A.: Deformable templates for face recognition. J. Cognitive Neuroscience 3(1), 59–70 (1991)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Markov, S., Birk, A. (2007). Detecting Humans in 2D Thermal Images by Generating 3D Models. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-74565-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74564-8
Online ISBN: 978-3-540-74565-5
eBook Packages: Computer ScienceComputer Science (R0)