In the context of team sport events monitoring, the various phases of the game must be delimited and interpreted. In the case of a basketball game, the detection and the tracking of the ball are mandatory.
The detection of the ball in a basketball game is not easy compared to the detection of the ball in a football or a tennis game. In fact, the ball is often occluded by players. Thus we deal with the detection of the ballistic trajectory of a ball thrown between two players or toward the basket.
We address the problem of the detection and the tracking of a ball in single-view and multi-view setting.
Our method is based on a foreground detector. It is divided into two consecutive parts and follows the “detect before tracking” approaches. The first part consists in 3D ball candidates detection and the second one in ballistic trajectory filtering based on a motion model. The second part is essential because, at some time-stamp, multiple detections as well as none happen.
Concerning the first part, two approaches, based on the foreground detector, are investigated to detect the 3D ball candidates. The correlation-based (Corr) and the connected component analysis (CCA) methods fundamentally differ in the way they process the foreground masks:
Concerning the second part, to remove false positive detection, temporal analysis is performed. Generally, the sources of false positives can be accredited to players reflect, digit on the score board, noise, etc. Fortunately, these false detections do not follow the typical ballistic trajectory of the ball in the air. We have proposed two approaches to detect this trajectory.
Video samples presenting the results associated to the paper 'Consensus-based trajectory estimation for ball detection in a calibrated cameras system' (submitted to the Journal of Real-Time Image Processing):
The filter videos refer to the performance obtained by filtering out the candidates that do not fit a ballistic model, while the trajectory videos display the performances by interpolating missed detections between the inliers, i.e. the candidate positions that follow a ballistic model, in addition to rejecting outliers.
The results are computed on the APIDIS dataset composed of 7 cameras (available here).
Part of the code is available here.
We present here under the videos associated to the tables measuring the ball detection accuracy in the paper ‘Consensus-based trajectory estimation for ball detection in a calibrated cameras system’. Red = detected candidates. Green = candidates that are validated based on trajectory analysis.
We present videos for which the ground truth is not available. It extends the quantitative assessment on a longer APIDIS video segment, and on video segments captured in other basketball courts.
Last updated September 19, 2017, at 12:12 PM