Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
http://link.springer.com/content/pdf/10.1007/s11263-016-0960-z.pdf
Reference49 articles.
1. Bae, S. H., & Yoon, K. J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In Proceedings of CVPR.
2. Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The clear MOT metrics. Journal on Image Video Processing. doi: 10.1155/2008/246309 .
3. Brau, E., Guan, J., Simek, K., Del Pero, L., Reimer Dawson, C., & Barnard, K. (2013). Bayesian 3D tracking from monocular video. In Proceedings of ICCV.
4. Brendel, W., Amer, M., & Todorovic, S. (2011). Multiobject tracking as maximum weight independent set. In Proceedings of CVPR.
5. Butt, A. A., & Collins, R. T. (2013). Multi-target tracking by Lagrangian relaxation to min-cost network flow. In Proceedings of CVPR.
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