Artificial Intelligence, an Overview: The Core Ingredients
Artificial Intelligence, an Overview: The Core Ingredients
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Statistical Cue Integration in DAG Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Soccer Players using the Graph Representation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Tracking soccer players aiming their kinematical motion analysis
Computer Vision and Image Understanding
Background recovering in outdoor image sequences: An example of soccer players segmentation
Image and Vision Computing
Multi-camera people tracking by collaborative particle filters and principal axis-based integration
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Automatic Tracking of Indoor Soccer Players Using Videos from Multiple Cameras
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
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There is a growing scientific interest in the study of tactical and physical attributes in futsal. These analyses use the players' motion, generally obtained with visual tracking, a task still not fully automated and that requires laborious guidance. In this regard, this work introduces an automatic procedure for estimating the positions of futsal players as probability distributions using multiple cameras and particle filters, reducing the need for human intervention during the process. In our framework, each player position is defined as a non-parametric distribution, which we track with the aid of particle filters. At every frame, we create the observation model by combining information from multiple cameras: a multimodal probability distribution function in court plane describing the likely players' positions. In order to decrease human intervention, we address the confusion between players during tracking using an appearance model to change and update the observation function. The experiments carried out reveal tracking errors below 70cm and enforce the method's potential for aiding sports teams in different technical aspects.