Extended MHT algorithm for multiple object tracking
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Camera pose estimation of a smartphone at a field without interest points
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Bagadus: an integrated system for arena sports analytics: a soccer case study
Proceedings of the 4th ACM Multimedia Systems Conference
Identification and tracking of players in sport videos
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
SportSense: using motion queries to find scenes in sports videos
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A comparative study on multi-person tracking using overlapping cameras
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Bagadus: An integrated real-time system for soccer analytics
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special issue of best papers of ACM MMSys 2013 and ACM NOSSDAV 2013
On-the-fly feature importance mining for person re-identification
Pattern Recognition
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
Online parameter tuning for object tracking algorithms
Image and Vision Computing
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In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a convex global optimization problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame to frame. We validate our approach on three multi-camera sport and pedestrian datasets that contain long and complex sequences. Our algorithm perseveres identities better than state-of-the-art algorithms while keeping similar MOTA scores.