Tracking and Labelling of Interacting Multiple Targets
Successful multi-target tracking requires solving two problems – localize the targets and label their identity. An isolated target’s identity can be unambiguously preserved from one frame to the next. However, for long sequences of many moving targets, like a football game, grouping scenarios will occur in which identity labellings cannot be maintained reliably by using continuity of motion or appearance. This paper describes how to match targets’ identities despite these interactions.
Trajectories of when a target is isolated are found. These trajectories end when targets interact and their labellings cannot be maintained. The interactions (merges and splits) of these trajectories form a graph structure. Appropriate feature vectors summarizing particular qualities of each trajectory are extracted. A clustering procedure based on these feature vectors allows the identities of temporally separated trajectories to be matched. Results are shown from a football match captured by a wide screen system giving a full stationary view of the pitch.
KeywordsFeature Vector Image Gradient Relative Depth Interaction Graph Foreground Pixel
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- 1.Khan, Z., Balch, T., Dellaert, F.: An mcmc-based particle filter for tracking multiple interacting targets. In: European Conference on Computer Vision (2004)Google Scholar
- 3.Xu, M., Orwell, J., Jones, G.: Tracking football players with multiple cameras. In: IEEE International Conference on Image Processing (2004)Google Scholar
- 4.Iwase, S., Saito, H.: Parallel tracking of all soccer players by integrating detected positions in multiple view images. In: ICPR, pp. 751–754 (2004)Google Scholar
- 5.Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: International Conference on Computer Vision (2003)Google Scholar
- 7.Needham, C., Boyle, R.: Tracking multiple sports players through occlusion, congestion and scale. In: BMVC (2001)Google Scholar
- 8.Figueroa, P., Leite, N., Barros, R., Cohen, I., Medioni, G.: Tracking soccer players using the graph representation. In: ICPR, pp. 787–790 (2004)Google Scholar
- 9.Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Conference on Computer Vision and Pattern Recognition (1999)Google Scholar