Video offers solutions to many of the traditional
problems with coach, trainer, commenter, umpires and other
security issues of modern team games. This paper presents a novel
framework to perform player identification and tracking
technique for the sports (Kabaddi) with extending the
implementation towards the event handling process which
expands the game analysis of the third umpire assessment. In the
proposed methodology, video preprocessing has done with Kalman
Filtering (KF) technique. Extended Gaussian Mixture Model
(EGMM) implemented to detect the object occlusions and player
labeling. Morphological operations have given the more genuine
results on player detection on the spatial domain by applying the
silhouette spot model. Team localization and player tracking has
done with Robust Color Table (RCT) model generation to classify
each team members. Hough Grid Transformation (HGT) and
Region of Interest (RoI) method has applied for background
annotation process. Through which each court line tracing and
labeling in the half of the court with respect to their state-of-art
for foremost event handling process is performed. Extensive
experiments have been conducted on real time video samples to
meet out the all the challenging aspects. Proposed algorithm
tested on both Self Developed Video (SDV) data and Real Time
Video (RTV) with dynamic background for the greater tracking
accuracy and performance measures in the different state of video
samples.