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.
Coming up with a purely content-based domain specific event extraction for untrimmed cricket telecast videos has not been attempted by many. @cite_14 tried to annotate the videos based by mapping the segments to the text-commentary using dynamic programming alignment, which may not always be available or may be noisy. Moreover, their dataset is quite small, as compared to what we are trying to achieve. Some other works have also looked at extraction of cricketing events but they have not considered it at such a scale as ours, or have not tried to generalize their solutions. @cite_8 and some similar works have tried to classify frames based on ground, pitch, or players present in them, or came up with a rule-based solution to classify motion features. None of them have analyzed their results on an entirely new set of cricket videos.
@cite_14 @cite_8
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The recognition of human activities is one of the key problems in video understanding. Action recognition is challenging even for specific categories of videos, such as sports, that contain only a small set of actions. Interestingly, sports videos are accompanied by detailed commentaries available online, which could be used to perform action annotation in a weakly-supervised setting. For the specific case of Cricket videos, we address the challenge of temporal segmentation and annotation of ctions with semantic descriptions. Our solution consists of two stages. In the first stage, the video is segmented into "scenes", by utilizing the scene category information extracted from text-commentary. The second stage consists of classifying video-shots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots. The novel aspect of this work is the fine temporal scale at which semantic information is assigned to the video. As a result of our approach, we enable retrieval of specific actions that last only a few seconds, from several hours of video. This solution yields a large number of labeled exemplars, with no manual effort, that could be used by machine learning algorithms to learn complex actions. Cricket broadcast video analysis has had difficulty identifying aspects of the content such as the type of batting stroke or the direction of the field played towards. Here we construct a composite feature combining Optical flow analysis along with camera view analysis to model the type of shots played. The work first presents an improved camera shot analysis based on learning parameters from a small supervision set. This splits the broadcast video into shots which are combined into balls and, the segment where the batsman is playing the stroke is identified. After that optical flow analysis is used to determine the direction of the stroke with an accuracy of 80 percent.