Abstract

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 ex- tract 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 Vec- tor Machines (SVM) classification, and evaluate on a publicly available European handball dataset. Results show that our approach can clas- sify 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 tra jectory-based methods. These methods extract activity features using the explicitly defined tra jectories, 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 distri- bution 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.