Indoor soccer has been of tactical and scientific interest, with applications dedicated to analyze tactical and physiological factors and also physical training. In both cases, the analysis is based on player tracking, done with human supervision. This paper presents an automatic tracking method which shows the trajectories of indoor soccer players during the game and saving skilled labor during the process. For this, we use a predictive filter to model the motion and the observation of multiple stationary cameras, strategically positioned around the court. We associate a particle filter to a robust probabilistic observation model with the measurement in court coordinates. The observation model proposed is based on data fusion across multiple camera coordinates and projected onto the court plane, creating a multimodal and bidirectional probability function, which represents the potential localization of players in the court plane. The probability function uses an appearance model to observe player's location, distinguishing very close players and yielding good weights in the observation model. The experimental results show tracking errors below 70 centimeters in most cases and indicate the potential of the method to help sports teams. © 2012 IEEE. 174181Figueroa, P.J., Leite, N.J., Barros, R.M.L., Tracking soccer players aiming their kinematical motion analysis (2006) Computer Vision and Image Understanding, 101 (2), pp. 122-135. , DOI 10.1016/j.cviu.2005.07.006, PII S1077314205001293Figueroa, P., Leite, N., Barros, R.M.L., Cohen, I., Medioni, G., Tracking soccer players using the graph representation (2004) ICPR, pp. 787-790. , Washington, DC, USAKasiri-Bidhendi, S., Safabakhsh, R., Effective tracking of the players and ball in indoor soccer games in the presence of occlusion (2009) ICC, pp. 524-529. , octOkuma, K., Taleghani, A., Freitas, N., Little, J., Lowe, D., A boosted particle filter: Multitarget detection and tracking (2004) ECCV, 3021, pp. 28-39Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) IEEE CVPR, 1, pp. 511-518Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D., Object detection with discriminatively trained part based models (2010) IEEE TPAMI, 32 (9), pp. 1627-1645Khan, S.M., Shah, M., A multiview approach to tracking people in crowded scenes using a planar homography constraint (2006) ECCVStauffer, C., Grimson, W., Adaptive background mixture models for real-time tracking (1999) IEEE CVPR, 2, pp. 252-260Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P., Sport player detection and tracking with a mixed network of planar and omnidirectional cameras (2009) ICDSCKang, J., Cohen, I., Medioni, G., Soccer player tracking across uncalibrated camera streams (2003) IEEE VS-PETS, pp. 172-179Gevarter, W.B., (1984) Robotics and Artificial Intelligence Applications Series: Overviews, , Business/Technology BooksForsyth, D., Ponce, J., (2002) Computer Vision: A Modern Approach, , Prentice HallGoldenstein, S.K., A gentle introduction to predictive filters (2004) RITA, 1, pp. 61-89Trucco, E., Verri, A., (1998) Introduction Technique for 3-D Computer Vision, , Prentice HallIsard, M., Blake, A., CONDENSATION - Conditional Density Propagation for Visual Tracking (1998) International Journal of Computer Vision, 29 (1), pp. 5-28Du, W., Piater, J., Multi-camera people tracking by collaborative particle filters and principal axis-based integration (2007) ACCV, (PART I), pp. 365-374. , Springer-VerlagBishop, C.M., (2006) Pattern Recognition and Machine Learning, , M. Jordan, J. Kleinberg, and B. Schölkopf, Eds. Springer