Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A trajectory-based analysis of coordinated team activity in a basketball game
Computer Vision and Image Understanding
Trajectory-based handball video understanding
Proceedings of the ACM International Conference on Image and Video Retrieval
Group Action Recognition Using Space-Time Interest Points
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Analysis of multi-agent activity using petri nets
Pattern Recognition
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Learning group activity in soccer videos from local motion
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Take your eyes off the ball: Improving ball-tracking by focusing on team play
Computer Vision and Image Understanding
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We introduce a novel approach for team activity recognition in sports. Given the positions of team players from a plan view of the playing field at any given time, we solve a particular Poisson equation to generate a smooth distribution defined on whole playground, termed the position distribution of the team. Computing the position distribution for each frame provides a sequence of distributions, which we process to extract motion features for team activity recognition. The motion features are obtained at each frame using frame differencing and optical flow. We investigate the use of the proposed motion descriptors with Support Vector Machines (SVM) classification, and evaluate on a publicly available European handball dataset. Results show that our approach can classify six different team activities and performs better than a method that extracts features from the explicitly defined positions. Our method is new and different from other trajectory-based methods. These methods extract activity features using the explicitly defined trajectories, where the players have specific positions at any given time, and ignore the rest of the playground. In our work, on the other hand, given the specific positions of the team players at a frame, we construct a position distribution for the team on the whole playground and process the sequence of position distribution images to extract motion features for activity recognition. Results show that our approach is effective.