Scene structure analysis for sprint sports

This work proposes a robust model to analyse the structure of horse races based on 2D velocity vector information. This model is capable of detecting scene breaks, classifying the view of the contenders and extracting the trajectory of the contenders throughout the race. The performance of the system is tested over six video clips from two different broadcast sources. The performance analysis shows the model achieves a high accuracy of view classification with the lowest value of 83%, all in real time

Macroblock Classification Method for Video Applications Involving Motions

In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame. Based on the proposed macroblock classification, we further propose algorithms for different video processing applications, including shot change detection, motion discontinuity detection, and outlier rejection for global motion estimation. Experimental results demonstrate that the methods based on the proposed approach can work effectively on these applications.Comment: This manuscript is the accepted version for TB (IEEE Transactions on Broadcasting