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SmarTrack - Multi-person Tracker
SmarTrack is a multi-camera person tracking system. It computes the ground location of people utilizing a coarse shape-plus-color signature, and is designed to work effectively in multi-person scenario where frequent and persistent occlusions occur among the persons. How it works in brief:
- For detection, a ground occupancy map is generated using motion features extracted from multiple views. The modes of the map represent those ground locations that most likely explain the image motion under a 3D human shape hypothesis. In a verification step, every mode of the occupancy map is tested for consistency of shape-model projection and extracted image motion: if confirmed, a new track is instantiated and a colour descriptor is extracted from shape-model projections to form a 3D shape-plus-appearance model of the new target.
- For tracking, a particle filter updates ground location hypotheses using these 3D shape-plus-appearance models. The course-of-dimension induced by appearance dependencies (notably, occlusions) leading to exponential complexity in multi-target tracking is hereby overcome: predictions are updated with a joint occlusion-aware shape-model projection under a quadratic complexity upper bound, leading to a scalable solution to multi-person tracking.
- O. Lanz: Approximate Bayesian Multibody Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 28(9):1436-1449, 2006.
- O. Lanz, S. Messelodi: A Sampling Algorithm for Occlusion Robust Multi Target Detection. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2009.
- T. Hu, S. Messelodi, O. Lanz: Dynamic Task Decomposition for Decentralized Object Tracking in Complex Scenes. Computer Vision and Image Understanding (CVIU), 134():89-104, 2015.
Recent Publications that use and/or extend:
- X. Alameda-Pineda, J. Staiano, R. Subramanian, L. M. Batrinca, E. Ricci, B. Lepri, O. Lanz, N. Sebe: SALSA: A Novel Dataset for Multimodal Group Behavior Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 38(8):1707-1720, 2016.
- Y. Yan, E. Ricci, G. Liu, O. Lanz, N. Sebe: A Multi-task Learning Framework for Head Pose Estimation under Target Motion, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 38(6):1070-1083, 2016.
- E. Ricci, J. Varadarajan, R. Subramanian, S. Rota Bulò, N. Ahuja, O. Lanz, "Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-formations from Surveillance Videos", International Conference on Computer Vision (ICCV), 2015.
- A.K. Rajagopal, R. Subramanian, E. Ricci, R.L. Vieriu, O. Lanz, R. Kalpathi, N. Sebe: Exploring Transfer Learning Approaches for Head Pose Classification from Multi-view Surveillance Images. International Journal of Computer Vision, 109(1-2):146-167, 2014.