Fine-Grain Annotation of Cricket Videos
Table 1: Evaluation of the video-shot recognition accuracy. A visual vocabulary using 1000 clusters of SIFT-features yields a considerably good performance, with the Linear-SVM.
['[BOLD] Kernel', '[BOLD] Linear', '[BOLD] Polynomial', '[BOLD] RBF', '[BOLD] Sigmoid']
We observe that the 1000 size vocabulary works better than 300. The Linear Kernel seems to suffice to learn the decision boundary, with a best-case accuracy of 82.25%. Refining the SVM predictions with the CRF based method yields an improved accuracy of 86.54. Specifically, the accuracy of the batsman/bowler shot categories is 89.68%.