Automatic Tracking Of Indoor Soccer Players Using Videos From Multiple Cameras

dc.creatorMorais E.
dc.creatorGoldenstein S.
dc.creatorFerreira A.
dc.creatorRocha A.
dc.date2012
dc.date2015-06-25T20:23:23Z
dc.date2015-11-26T15:18:46Z
dc.date2015-06-25T20:23:23Z
dc.date2015-11-26T15:18:46Z
dc.date.accessioned2018-03-28T22:28:23Z
dc.date.available2018-03-28T22:28:23Z
dc.identifier9780769548296
dc.identifierBrazilian Symposium Of Computer Graphic And Image Processing. , v. , n. , p. 174 - 181, 2012.
dc.identifier15301834
dc.identifier10.1109/SIBGRAPI.2012.32
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84872382045&partnerID=40&md5=3850d04d59703ed9a8ec518557bd8cc3
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/90028
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/90028
dc.identifier2-s2.0-84872382045
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1259617
dc.descriptionIndoor 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.
dc.description
dc.description
dc.description174
dc.description181
dc.descriptionFigueroa, 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 S1077314205001293
dc.descriptionFigueroa, 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, USA
dc.descriptionKasiri-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. , oct
dc.descriptionOkuma, K., Taleghani, A., Freitas, N., Little, J., Lowe, D., A boosted particle filter: Multitarget detection and tracking (2004) ECCV, 3021, pp. 28-39
dc.descriptionViola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) IEEE CVPR, 1, pp. 511-518
dc.descriptionFelzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D., Object detection with discriminatively trained part based models (2010) IEEE TPAMI, 32 (9), pp. 1627-1645
dc.descriptionKhan, S.M., Shah, M., A multiview approach to tracking people in crowded scenes using a planar homography constraint (2006) ECCV
dc.descriptionStauffer, C., Grimson, W., Adaptive background mixture models for real-time tracking (1999) IEEE CVPR, 2, pp. 252-260
dc.descriptionAlahi, A., Boursier, Y., Jacques, L., Vandergheynst, P., Sport player detection and tracking with a mixed network of planar and omnidirectional cameras (2009) ICDSC
dc.descriptionKang, J., Cohen, I., Medioni, G., Soccer player tracking across uncalibrated camera streams (2003) IEEE VS-PETS, pp. 172-179
dc.descriptionGevarter, W.B., (1984) Robotics and Artificial Intelligence Applications Series: Overviews, , Business/Technology Books
dc.descriptionForsyth, D., Ponce, J., (2002) Computer Vision: A Modern Approach, , Prentice Hall
dc.descriptionGoldenstein, S.K., A gentle introduction to predictive filters (2004) RITA, 1, pp. 61-89
dc.descriptionTrucco, E., Verri, A., (1998) Introduction Technique for 3-D Computer Vision, , Prentice Hall
dc.descriptionIsard, M., Blake, A., CONDENSATION - Conditional Density Propagation for Visual Tracking (1998) International Journal of Computer Vision, 29 (1), pp. 5-28
dc.descriptionDu, W., Piater, J., Multi-camera people tracking by collaborative particle filters and principal axis-based integration (2007) ACCV, (PART I), pp. 365-374. , Springer-Verlag
dc.descriptionBishop, C.M., (2006) Pattern Recognition and Machine Learning, , M. Jordan, J. Kleinberg, and B. Schölkopf, Eds. Springer
dc.languageen
dc.publisher
dc.relationBrazilian Symposium of Computer Graphic and Image Processing
dc.rightsfechado
dc.sourceScopus
dc.titleAutomatic Tracking Of Indoor Soccer Players Using Videos From Multiple Cameras
dc.typeActas de congresos


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