Parallel Tracking of All Soccer Players by Integrating Detected Positions in Multiple View Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-world tracking of multiple interacting targets for indoor-sports applications
Computer Vision and Image Understanding
A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Multiple Object Tracking Using K-Shortest Paths Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Long term real trajectory reuse through region goal satisfaction
MIG'11 Proceedings of the 4th international conference on Motion in Games
Tracking multiple people under global appearance constraints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
On-the-fly feature importance mining for person re-identification
Pattern Recognition
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We present a novel multi-modal fusion framework for non-sequential person detection, localization and identification from multiple views. Our goal is independent processing of randomly-accessed sections of video, either individual frames or small batches thereof. This way, we aim to limit the error propagation that makes the existing approaches unsuitable for fully-autonomous tracking of multiple people in long video sequences. Our framework uses one or more trained classifiers to fuse multiple weak feature maps. We perform experimental validation on a challenging dataset, demonstrating how the framework can, depending on the provided feature maps, be used either only to improve generic person detection, or enable simultaneous detection and recognition of individuals. Finally, we show that tracking-by-identification using the output of the proposed framework outperforms the state-of-the-art identification-by-tracking approach in terms of preserved track identities.