  In this paper, we deal with the problem of temporal action localization for a
large-scale untrimmed cricket videos dataset. Our action of interest for
cricket videos is a cricket stroke played by a batsman, which is, usually,
covered by cameras placed at the stands of the cricket ground at both ends of
the cricket pitch. After applying a sequence of preprocessing steps, we have
~73 million frames for 1110 videos in the dataset at constant frame rate and
resolution. The method of localization is a generalized one which applies a
trained random forest model for CUTs detection(using summed up grayscale
histogram difference features) and two linear SVM camera models(CAM1 and CAM2)
for first frame detection, trained on HOG features of CAM1 and CAM2 video
shots. CAM1 and CAM2 are assumed to be part of the cricket stroke. At the
predicted boundary positions, the HOG features of the first frames are computed
and a simple algorithm was used to combine the positively predicted camera
shots. In order to make the process as generic as possible, we did not consider
any domain specific knowledge, such as tracking or specific shape and motion
features.
  The detailed analysis of our methodology is provided along with the metrics
used for evaluation of individual models, and the final predicted segments. We
achieved a weighted mean TIoU of 0.5097 over a small sample of the test set.
