Artículos de revistas
A Multiple Camera Methodology For Automatic Localization And Tracking Of Futsal Players
Pattern Recognition Letters. , v. 39, n. 1, p. 21 - 30, 2014.
There is a growing scientific interest in the study of tactical and physical attributes in futsal. These analyses use the players' motion, generally obtained with visual tracking, a task still not fully automated and that requires laborious guidance. In this regard, this work introduces an automatic procedure for estimating the positions of futsal players as probability distributions using multiple cameras and particle filters, reducing the need for human intervention during the process. In our framework, each player position is defined as a non-parametric distribution, which we track with the aid of particle filters. At every frame, we create the observation model by combining information from multiple cameras: a multimodal probability distribution function in court plane describing the likely players' positions. In order to decrease human intervention, we address the confusion between players during tracking using an appearance model to change and update the observation function. The experiments carried out reveal tracking errors below 70 cm and enforce the method's potential for aiding sports teams in different technical aspects. © 2013 Elsevier B.V. All rights reserved.3912130Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P., Sport player detection and tracking with a mixed network of planar and omnidirectional cameras (2009) ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1-8Barros, R.M., Menezes, R.P., Russomanno, T.G., Misuta, M.S., Brandao, B.C., Figueroa, P.J., Leite, N.J., Goldenstein, S.K., Measuring handball players trajectories using an automatically trained boosting algorithm (2011) Computer Methods in Biomechanics and Biomedical Engineering, 14 (1), pp. 53-63Bishop, C., (2006) Pattern Recognition and Machine Learning, , SpringerDe Morais, E.F., Goldenstein, S., Ferreira, A., Rocha, A., Automatic tracking of indoor soccer players using videos from multiple cameras (2012) 25th Conference on Graphics, Patterns and Images, Ouro Preto, Brazil, pp. 174-181Du, W., Piater, J., Multi-camera people tracking by collaborative particle filters and principal axis-based integration (2007) Asian Conference on Computer Vision, VOL. PART I, pp. 365-374. , Springer-VerlagFelzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D., Object detection with discriminatively trained part based models (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (9), pp. 1627-1645Figueroa, P., Leite, N., Barros, R.M.L., Cohen, I., Medioni, G., Tracking soccer players using the graph representation (2004) International Conference on Pattern Recognition, pp. 787-790. , Washington, DC, USAFigueroa, 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.J., Leite, N.J., Barros, R.M.L., Background recovering in outdoor image sequences: An example of soccer players segmentation (2006) Image and Vision Computing, 24 (4), pp. 363-374. , DOI 10.1016/j.imavis.2005.12.012, PII S0262885606000059Forsyth, D., Ponce, J., (2002) Computer Vision: A Modern Approach, , Prentice HallGevarter, W.B., (1984) Robotics and Artificial Intelligence Applications Series: Overviews, Business/Technology Books.Goldenstein, S., A gentle introduction to predictive filters (2004) Journal of Theoretical and Applied Computing, 1, pp. 61-89Goldenstein, S., Vogler, C., Metaxas, D., Statistical cue integration in DAG deformable models (2003) IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (7), pp. 801-813Goldenstein, S., Vogler, C., Metaxas, D., 3D facial tracking from corrupted movie sequences (2004) Proceedings of IEEE Computer Vision And, Pattern Recognition.Gray, A., Jenkins, D., Andrews, M., Taaffe, D., Glover, M., Validity and reliability of GPS for measuring distance travelled in field-based team sports (2010) Journal of Sports Sciences, 28, pp. 1319-1325Isard, M., Blake, A., CONDENSATION - Conditional Density Propagation for Visual Tracking (1998) International Journal of Computer Vision, 29 (1), pp. 5-28Juang, C.-F., Sun, W.-K., Chen, G.-C., Object detection by color histogram-based fuzzy classifier with support vector learning (2009) Journal of Neurocomputing, 72 (1012), pp. 2464-2476Kang, J., Cohen, I., Medioni, G., Soccer player tracking across uncalibrated camera streams (2003) IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 172-179Kasiri-Bidhendi, S., Safabakhsh, R., Effective tracking of the players and ball in indoor soccer games in the presence of occlusion (2009) International Computer Conference, pp. 524-529Khan, S.M., Shah, M., A multiview approach to tracking people in crowded scenes using a planar homography constraint (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3954 LNCS, pp. 133-146. , Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, ProceedingsMendez-Villanueva, A., Buchheit, M., Simpson, B., Bourdon, P.C., Match play intensity distribution in youth soccer (2013) International Journal of Sports Medicine, 2 (34), pp. 101-110Miura, J., Kubo, H., Tracking players in highly complex scenes in broadcast soccer video using a constraint satisfaction approach (2008) International Conference on Content-Based Image and Video Retrieval, pp. 505-514Morais, E., Goldenstein, S., Rocha, A., Automatic localization of indoor soccer players from multiple cameras (2012) International Conference on Computer Vision Theory and Applications, pp. 205-212Okuma, K., Taleghani, A., Freitas, N., Little, J., Lowe, D., A boosted particle filter: Multitarget detection and tracking (2004) European Conference on Computer Vision, 3021, pp. 28-39Stauffer, C., Grimson, W., Adaptive background mixture models for real-time tracking (1999) IEEE International Conference on Computer Visiona and Pattern Recognition, 2, pp. 252-260Trucco, E., Verri, A., (1998) Introduction Technique for 3-D Computer Vision, , Prentice HallViola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) IEEE International Conference on Computer Vision and Pattern Recognition, 1, pp. 511-518