Abstract: Self-checkout verification systems and methods are described. One aspect includes receiving a plurality of images from a camera, the images being associated with a customer self-checkout process. The images may be analyzed to detect one or more items in possession of the customer, count a first number of the items, categorize each item, and construct a first category set including the categorizing for all the items. One aspect includes receiving a point-of-sale record at a completion of the self-checkout process, the point-of-sale record including a second number of the items and a second category set including the categorizing for all the items. The first number and the second number, and the first category set and the second category set may be compared. An alert may be generated if there is a discrepancy between the first number and the second number, or the first category set and the second category set.

Abstract: A system provides automated editing of a media file. Frames of a media file are extracted and feature vectors are generated based thereon. The feature vectors are clustered according to similarity of the content of the feature vectors and the temporal proximity of frames corresponding to the feature vectors in the media file. Clusters are ranked and selected according to the ranking. Segments of the media file corresponding to the selected clusters are combined into a highlight video. Hotspots in a media file may be identified in a media file by detecting local maxima in a measure of movement of pixels between frames. Clusters may be ranked and selected according to an iterative algorithm that identifies clusters that are the most different from an average of the feature vectors and from other selected clusters.

Abstract: A system provides automated editing of a media file. Frames of a media file are extracted and feature vectors are generated based thereon. The feature vectors are clustered according to similarity of the content of the feature vectors and the temporal proximity of frames corresponding to the feature vectors in the media file. Clusters are ranked and selected according to the ranking. Segments of the media file corresponding to the selected clusters are combined into a highlight video. Hotspots in a media file may be identified in a media file by detecting local maxima in a measure of movement of pixels between frames. Clusters may be ranked and selected according to an iterative algorithm that identifies clusters that are the most different from an average of the feature vectors and from other selected clusters.