Xuan Song
Skip Abstract Section
Abstract
Multitarget tracking becomes significantly more challenging when the targets are in close proximity or frequently interact with each other. This article presents a promising online system to deal with these problems. The novelty of this system is that laser and vision are integrated with tracking and online learning to complement each other in one framework: when the targets do not interact with each other, the laser-based independent trackers are employed and the visual information is extracted simultaneously to train some classifiers online for “possible interacting targets”. When the targets are in close proximity, the classifiers learned online are used alongside visual information to assist in tracking. Therefore, this mode of cooperation not only deals with various tough problems encountered in tracking, but also ensures that the entire process can be completely online and automatic. Experimental results demonstrate that laser and vision fully display their respective advantages in our system, and it is easy for us to obtain a good trade-off between tracking accuracy and the time-cost factor.
- Arras, K., Grzonka, S., Luber, M., and Burgard, W. 2008. Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities. In Proceedings of the IEEE International Conference on Robotics and Automation. 1710--1715.Google Scholar
- Arras, K., Tomatis, N., and Siegwart, R. 2000. Multisensor on-the-fly localization using laser and vision. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 462--467.Google Scholar
- Avidan, S. 2007. Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2, 261--271. Google ScholarDigital Library
- Bar-Shalom, Y. and Fortmann, T. E. 1998. Tracking and data association. Academic Press, New York. Google ScholarDigital Library
- Bar-Shalom, Y. and Li, X. 1995. Multitarget-Multisensor Tracking: Principles and Techniques. YBS Publishing, Storrs, CT.Google Scholar
- Bellotto, N. and Hu, H. 2006. Vision and laser data fusion for tracking people with a mobile robot. In Proceedings of the IEEE International Conference on Robotics and Biomimetics. 7--12.Google Scholar
- Blackman, S. and Popoli, R. 1999. Design and Analysis of Modern Tracking Systems. Artech House, Norwood, MA.Google Scholar
- Blanco, J., Burgard, W., Sanz, R., and Fernandez, J. L. 2003. Fast face detection for mobile robots by integrating laser range data with vision. In Proceedings of the IEEE International Conference on Robotics and Automation. 625--631.Google Scholar
- Breiman, L., Friedman, J., Olshen, R., and Stone, C. 1984. Classification and Regression Trees. Chapman Hall, New York.Google Scholar
- Broida, T. and R.Chellappa. 1986. Estimation of object motion parameters from noisy images. IEEE Trans. Pattern Anal. Mach. Intell. 8, 1, 90--99. Google ScholarDigital Library
- Cai, Y., Freitas, N. D., and Little, J. J. 2006. Robust visual tracking for multiple targets. In Proceedings of the European Conference on Computer Vision. 125--135. Google ScholarDigital Library
- Chakravarty, P. and Jarvis, R. 2006. Panoramic vision and laser range finder fusion for multiple person tracking. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2949--2954.Google Scholar
- Cielniak, G., Duckett, T., and Lilienthal, A. J. 2007. Improved data association and occlusion handling for vision-based people tracking by mobile robots. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 3436--3441.Google Scholar
- Comaniciu, D. and Meer, P. 1999. Distribution free decomposition of multivariate data. Pattern Anal. Appli. 38, 22--30.Google ScholarDigital Library
- Cui, J., Zha, H., Zhao, H., and Shibasaki, R. 2008. Multi-Modal tracking of people using laser scanners and video camera. Image and Vision Computing. 26, 240--252. Google ScholarDigital Library
- Doucet, A., Godsill, S. J., and Andrieu, C. 2000. On sequential monte carlo sampling methods for bayesian filtering. Statist. Comput. 10, 1, 197--208. Google ScholarDigital Library
- Fod, A., Howard, A., and Mataric, M. J. 2002. A laser-based people tracker. In Proceedings of the IEEE International Conference on Robotics and Automation. 3024--3029.Google Scholar
- Gennari, G. and Hager, G. 2004. Probabilistic data association methods in visual tracking of groups. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 876--881. Google ScholarDigital Library
- Goodman, I., Mahler, R., and Nguyen, H. 1997. Mathematics of Data Fusion. Kluwer Academic Publishers. Google ScholarDigital Library
- Grabner, H. and Bischof, H. 2006. On-Line boosting and vision. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 260--267. Google ScholarDigital Library
- Huang, J., Guestrin, C., and Guibas, L. 2009. Fourier theoretic probabilistic inference over permutations. J. Mach. Learn. Res., 997--1070. Google ScholarDigital Library
- Jiang, H., Fels, S., and Little, J. 2007. A linear programming approach for multiple object tracking. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 1380--1387.Google Scholar
- Khan, Z., Balch, T., and Dellaert, F. 2005. Mcmc-Based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27, 2, 1805--1819. Google ScholarDigital Library
- Khan, Z., Balch, T., and Dellaert, F. 2006. Mcmc data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1, 1960--1972. Google ScholarDigital Library
- Kuo, C., Huang, C., and Nevatia, R. 2010. Multi-Target Tracking by on-line learned discriminative appearance models. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 685--692.Google Scholar
- Lanz, O. and Manduchi, R. 2005. Hybrid joint-separable multibody tracking. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 413--420. Google ScholarDigital Library
- Leibe, B., Schindler, K., Cornelis, N., and Gool, L. V. 2008. Coupled detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. 1683--1698. Google ScholarDigital Library
- Levi, K. and Weiss, Y. 2004. Learning object detection from a small number of examples: The importance of good features. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 53--60. Google ScholarDigital Library
- Li, Y., Ai, H., Yamashita, T., Lao, S., and Kawade, M. 2007. Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life-spans. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 1--8.Google Scholar
- Lu, L. and Hager, G. 2006. Dynamic foreground/background extraction from images and videos using random patches. In Proceedings of the Conference on Neural Information Processing Systems. 351--358.Google Scholar
- Mahler, R. 2003. Multi-Target bayes filtering via first-order multi-target moments. IEEE Trans. Aerospace Electron. Syst. 39, 4, 1152--1178.Google ScholarCross Ref
- Mahler, R. 2007a. Phd filters of higher order in target number. IEEE Trans. Aerospace Electron. Syst. 43, 3, 1152--1178.Google ScholarCross Ref
- Mahler, R. 2007b. Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood, MA. Google ScholarDigital Library
- Nillius, P., Sullivan, J., and Carlsson, S. 2006. Multi-Target tracking - linking identities using bayesian network inference. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 2187--2194. Google ScholarDigital Library
- Oh, S., Russell, S., and Sastry, S. 2004. Markov chain monte carlo data association for general multiple target tracking problems. In Proceedings of the IEEE Conference on Decision and Control. 735--742.Google Scholar
- Okuma, K., Taleghani, A., Freitas, N. D., Little, J. J., and Lowe, D. G. 2004. A boosted particle filter: Multitarget detection and tracking. In Proceedings of the European Conference on Computer Vision. 28--39.Google Scholar
- Oliveira, L., Nunes, U., Peixoto, P., and Silva, M. 2010. Semantic fusion of laser and vision in pedestrian detection. Pattern Recogn. 3648--3659. Google ScholarDigital Library
- Qu, W., Schonfeld, D., and Mohamed, M. 2005. Real-Time interactively distributed multi-object tracking using a magnetic-inertia potential model. In Proceedings of the IEEE International Conference on Computer Vision. 535--540. Google ScholarDigital Library
- Rasmussen, C. and Hager, G. 2001. Probabilistic data association methods for tracking complex visual objects. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1, 560--576. Google ScholarDigital Library
- Read, D. 1979. An algorithm for tracking multiple targets. IEEE Trans. Automa. Control 24, 1, 84--90.Google Scholar
- Scheutz, M., McRaven, J., and Cserey, G. 2004. Fast, reliable, adaptive bimodal people tracking for indoor environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 1347--1352.Google Scholar
- Schulz, D. 2006. A probabilistic exemplar approach to combine laser and vision for person tracking. In Proceedings of the Robotics: Science and Systems Conference. 362--367.Google ScholarCross Ref
- Schulz, D., Burgard, W., Fox, D., and Cremers, A. 2003. People tracking with a mobile robot using sample-based joint probabilistic data association filters. Inte. J. Robot. Res. 22, 2, 99--116.Google ScholarCross Ref
- Shao, X., Zhao, H., Nakamura, K., Katabira, K., Shibasaki, R., and Nakagawa, Y. 2007. Detection and tracking of multiple pedestrians by using laser range scanners. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2174--2179.Google Scholar
- Shitrit, H. B., Berclaz, J., Fleuret, F., and Fua, P. 2011. Tracking multiple people under global appearance constraints. In Proceedings of the IEEE International Conference on Computer Vision. 1--8. Google ScholarDigital Library
- Song, X., Cui, J., Wang, X., Zhao, H., and Zha, H. 2008a. Tracking interacting targets with laser scanner via on-line supervised learning. In Proceedings of the IEEE International Conference on Robotics and Automation. 2271--2276.Google Scholar
- Song, X., Cui, J., Zha, H., and Zhao, H. 2008b. Probabilistic detection-based particle filter for multi-target tracking. In Proceedings of the British Machine Vision Conference. 223--232.Google Scholar
- Song, X., Cui, J., Zha, H., and Zhao, H. 2008c. Vision-Based multiple interacting targets tracking via on-line supervised learning. In Proceedings of the European Conference on Computer Vision. 642--655. Google ScholarDigital Library
- Song, X., Cui, J., Zhao, H., and Zha, H. 2008d. Bayesian fusion of laser and vision for multiple people detection and tracking. In Proceedings of the of IEEE International Conference on Instrumentation, Control and Information Technology. 3014--3019.Google Scholar
- Song, X., Shao, X., Zhao, H., Cui, J., Shibasaki, R., and Zha, H. 2010a. An online approach: Learning-Semantic-Scene-By-Tracking and tracking-by-learning-semantic-scene. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 739--746.Google Scholar
- Song, X., Zhao, H., Cui, J., Shao, X., Shibasaki, R., and Zha, H. 2010b. Fusion of laser and vision for multiple targets tracking via online learning. In Proceedings of the IEEE International Conference on Robotics and Automation. 406-411.Google Scholar
- Spinello, L. and Siegwart, R. 2008. Human detection using multimodal and multidimensional features. In Proceedings of the IEEE International Conference on Robotics and Automation. 3264--3269.Google Scholar
- Sullivan J. and Carlsson, S. 2006. Tracking and labeling of interacting multiple targets. In Proceedings of the European Conference on Computer Vision. 661--675. Google ScholarDigital Library
- Ulmke, M., Erdinc, O., and Willett, P. 2010. Gmti tracking via the gaussian mixture cardinalized probability hypothesis density filter. IEEE Trans. Aerospace Electron. Syst. 46, 4, 1821--1833.Google ScholarCross Ref
- Vermaak, J., Doucet, A., and Perez, P. 2003. Maintaining multi-modality through mixture tracking. In Proceedings of the IEEE International Conference on Computer Vision. 1110--1116. Google ScholarDigital Library
- Vermaak, J., Godsill, S., and Perez, P. 2005. Monte carlo filtering for multi target tracking and data association. IEEE Trans. Aerospace Electron. Syst. 41, 1, 309--332.Google ScholarCross Ref
- Vo, B. and Ma, W. 2006. The gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process. 54, 11, 4091--4104. Google ScholarDigital Library
- Vo, B. and Ma, W. 2009. The cardinality balanced multi-target multi-bernoulli filter and its implementations. IEEE Trans. Signal Process. 57, 2, 409C423. Google ScholarDigital Library
- Vo, B., Singh, S., and Doucet, A. 2005. Sequential monte carlo methods for multi-target filtering with random finite sets. IEEE Trans. Aerospace Electron. Syst. 41, 4, 1224--1245.Google ScholarCross Ref
- Yilmaz, A., Javed, O., and Shah, M. 2006. Object tracking: A survey. ACM Comput. Surv., 3--47. Google ScholarDigital Library
- Yu, Q., Medioni, G., and Cohen, I. 2007. Multiple target tracking using spatio-temporal markov chain monte carlo data association. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 642--649.Google Scholar
- Yu, T. and Wu, Y. 2005. Decentralized multiple target tracking using netted collaborative autonomous trackers. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 939--946. Google ScholarDigital Library
- Zhang, L., Li, Y., and Nevatia, R. 2008. Global data association for multi-object tracking using network flows. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 1--8.Google Scholar
- Zhao, H. and R.Shibasaki. 2005. A novel system for tracking pedestrians using multiple single-row laser range scanners. IEEE Trans. Syst., Man Cybernet. A 35, 2, 283--291. Google ScholarDigital Library
- Zhao, T. and Nevatia, R. 2004. Tracking multiple humans in complex situations. IEEE Trans. Pattern Anal. Mach. Intell. 7, 1, 1208--1221. Google ScholarDigital Library
Index Terms
- An online system for multiple interacting targets tracking: Fusion of laser and vision, tracking and learning
Recommendations
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
Occlusion and lack of visibility in crowded and cluttered scenes make it difficult to track individual people correctly and consistently, particularly in a single view. We present a multi-view approach to solving this problem. In our approach we neither ...
A solution of multiple targets tracking for linear CCD based optical positioning system applied to image guided surgery
AICI'11: Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part IIDoctors often need to use several surgery instruments at the same time during Image Guided Surgery (IGS). However, it's hard for linear CCD based Optical Positioning System (OPS) to maintain both a high frequency and efficiency when tracking multiple ...
Tracking multiple interacting targets in a camera network
We propose a tracker for multiple interacting targets in a camera network.A model is developed to decide the group state of each trajectory.The tracking problem is converted to a network flow problem. In this paper we propose a framework for tracking ...
Comments