Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy. 1. Introduction Because of the advance in surveillance systems, object tracking has been an active research topic in the computer vision community over the last two decades as it is an essential prerequisite for analyzing and understanding video data. However, tracking an object, in general, is a challenging problem. Difficulties in tracking objects may arise due to several reasons such as abrupt object motion, changing appearance patterns of the object and/or the scene, nonrigid object structures, partial/full object-to-object and object-to-scene occlusions, camera motion, loss of information caused by projection of the 3D world on a 2D image, noise in images, complex object motion, complex object shapes, and real-time processing requirements. Moreover, tracking is usually performed in the context of higher-level applications, which in turn require the location and/or shape of the object in every captured frame. Accordingly, several assumptions should be considered to constrain the tracking problem for a particular application. A great deal of interest in the field of object tracking has been generated due to (i) the recent evolution of high-speed computers, (ii) the availability of high quality and inexpensive sensors (video cameras), and (iii) the increasing demand for an automated real-time video analysis. Tracking an object is to
References
[1]
C.-C. Chia, W.-K. Chan, and S.-Y. Chien, “Cooperative surveillance system with fixed camera object localization and mobile robot target tracking,” in Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology (PSIVT '09), vol. 5414 of Lecture Notes in Computer Science, pp. 886–897, Springer, Tokyo, Japan, 2009.
[2]
W. Niu, L. Jiao, D. Han, and Y.-F Wang, “Real-time multi-person tracking in video surveillance,” in Proceedings of the 4th IEEE Pacific Rim Multimedia Conference (PCM '03), pp. 1–5, Singapore, December 2003.
[3]
M. T？ndl, T. Stark, N. Erol, F. L？er, and A. Kecskeméthy, “An object-oriented approach to simulating human gait motion based on motion tracking,” International Journal of Applied Mathematics and Computer Science, vol. 19, no. 3, pp. 469–483, 2009.
[4]
D.-X. Lai, Y.-H. Chang, and Z.-H. Zhong, “Active contour tracking of moving objects using edge flows and ant colony optimization in video sequences,” in Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology (PSIVT '09), vol. 5414 of Lecture Notes in Computer Science, pp. 1104–1116, Springer, Tokyo, Japan, 2009.
[5]
B. Sugandi and H. Kim, “A moving object tracking based on color information employing a particle filter algorithm,” in Proceedings of the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, 2009.
[6]
J. Kang, I. Cohen, and G. Medioni, “Soccer player tracking across uncalibrated camera streams,” in Proceedings of the IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (PETS '03), Nice, France, October 2003.
[7]
M. Mozerov, A. Amato, X. Roca, and J. González, “Solving the multi object occlusion problem in a multiple camera tracking system,” Pattern Recognition and Image Analysis, vol. 19, no. 1, pp. 165–171, 2009.
[8]
O. Javed and M. Shah, “Tracking and object classification for automated surveillance,” in Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, May 2002.
[9]
L. J. Latecki and R. Miezianko, “Object tracking with dynamic template update and occlusion detection,” in Proceedings of the 18th IEEE International Conference on Pattern Recognition (ICPR '06), vol. 1, pp. 556–560, Hong Kong, August 2006.
[10]
T. Ellis, D. Makris, and J. Black, “Learning a multi-camera topology,” in Proceedings of the Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 165–171, Nice, France, October, 2003.
[11]
D. Setterwall, Computerized video analysis of football—technical and commercial possibilities for football coaching, M.S. thesis, Department of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden, 2004.
[12]
M. Xu, J. Orwell, L. Lowey, and D. Thirds, “Architecture and algorithms for tracking football players with multiple cameras,” IEE Proceedings: Vision, Image and Signal Processing, vol. 152, no. 2, pp. 232–241, 2005.
[13]
R. T. Collins, A. J. Lipton, H. Fujiyoshi, and T. Kanade, “Algorithms for cooperative multisensor surveillance,” Proceedings of the IEEE, vol. 89, no. 10, pp. 1456–1477, 2001.
[14]
C. J. Needham and R. D. Boyle, “Performance evaluation metrics and statistics for positional tracker evaluation,” in Proceedings of the International Conference on Vision Systems (ICVS '03), pp. 278–289, Graz, Austria, 2003.
[15]
M. Xu, J. Orwell, and G. Jones, “Tracking football players with multiple cameras,” in Proceedings of the International Conference on Image Processing (ICIP '04), vol. 5, pp. 2909–2912, Singapore, October 2004.
[16]
R. Tsai, “An efficient and accurate camera calibration technique for 3D machine vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '86), pp. 364–374, Miami Beach, Fla, USA, 1986.
[17]
S. Iwase and H. Saito, “Tracking soccer players based on homography among multiple views,” in Visual Communications and Image Processing, vol. 5150 of Proceedings of SPIE, pp. 283–292, Lugano, Switzerland, July 2003.
[18]
S. Iwase and H. Saito, “Tracking soccer player using multiple views,” in Proceedings of the 8th IAPR Workshop on Machine Vision Applications (MVA '02), Nara, Japan, December, 2002.
[19]
J. B. Hayet, J. H. Piater, and J. G. Verly, “Fast 2D model-to-image registration using vanishing points for sports video analysis,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), pp. 417–420, Genoa, Italy, September 2005.
[20]
J. Sullivan and S. Carlsson, “Tracking and labelling of interacting multiple targets,” in Proceedings of the 9th European Conference on Computer Vision (ECCV '06), vol. 3953 of Lecture Notes in Computer Science, pp. 619–632, Graz, Austria, May 2006.
[21]
S. Cheung and C. Kamath, “Robust techniques for background subtraction in urban traffic video,” in Visual Communications and Image Processing, vol. 5308 of Proceedings of SPIE, pp. 881–892, San Jose, Calif, USA, January 2004.
[22]
R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting moving objects, ghosts, and shadows in video streams,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1337–1342, 2003.
[23]
P. Remagnino, A. Baumberg, T. Grove, et al., “An integrated traffic and pedestrian model-based vision system,” in Proceedings of the 8th British Machine Vision Conference (BMVC '97), A. F. Clark, Ed., pp. 380–389, Essex, UK, 1997.
[24]
R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, pp. 35–45, 1960.
[25]
A. Prati, I. Mikic, M. M. Trivedi, and R. Cucchiara, “Detecting moving shadows: algorithms and evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918–923, 2003.
[26]
R. Rosales and S. Sclaroff, “Improved tracking of multiple humans with trajectory prediction and occlusion modeling,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '98), Santa Barbara, Calif, USA, June 1998.
[27]
N. D. Doulamis, Coupled Multi-Object Tracking and Labeling for Vehicle Trajectory Estimation and Matching, Springer Science+Business Media LLC, 2009.
[28]
A. Farina and S. Pardini, “Track-while-scan algorithm in a clutter environment,” IEEE Transactions on Aerospace and Electronic Systems, vol. 14, no. 5, pp. 769–779, 1978.
[29]
Y. Bar-Shalom and T. Fortmann, Tracking and Data Association, Academic Press, 1988.
[30]
D. Reid, “An algorithm for tracking multiple targets,” IEEE Transactions on Automatic Control, vol. 24, no. 6, pp. 843–854, 1979.
[31]
S. Blackman, Multiple Target Tracking with Radar Applications, Artech House, 1986.
[32]
L. Klein, Sensor and Data Fusion Concepts and Applications, Bellingham, Wash, USA, SPIE Optical Engineering Press, 1993.
[33]
A. Gad and M. Farooq, “Tracking highly maneuvering targets in clutter using interacting multiple model fuzzy logic based tracker,” in Signal Processing, Sensor Fusion, and Target Recognition XI, vol. 4729 of Proceedings of SPIE, pp. 23–34, Orlando, Fla, USA, April 2002.
[34]
A. Gad and M. Farooq, “Viterbi-based data association techniques for target tracking,” in Signal Processing, Sensor Fusion, and Target Recognition XII, Proceedings of SPIE, pp. 37–46, Orlando, Fla, USA, April 2003.
[35]
F. White, “A model for data fusion,” in Proceedings of the 1st International Symposium on Sensor Fusion, 1988.
[36]
M. Bedworth, “Probability moderation for multilevel information processing,” DRA Technical Report DRA CIS(SEI)/651/8/M94.AS03BPO32/1, 1994.
[37]
M. Bedworth and J. O'Brien, “Omnibus model: a new model of data fusion?” IEEE Aerospace and Electronic Systems Magazine, vol. 15, no. 4, pp. 30–36, 2000.
