Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: A system for indexing animated content receives detections extracted from a media file, where each one of the detections includes an image extracted from a corresponding frame of the media file that corresponds to a detected instance of an animated character. The system determines, for each of the received detections, an embedding defining a set of characteristics for the detected instance. The embedding associated with each detection is provided to a grouping engine that is configured to dynamically configure at least one grouping parameter based on a total number of the detections received. The grouping engine is also configured to sort the detections into groups using the grouping parameter and the embedding for each detection. A character ID is assigned to each one of the groups of detections, and the system indexes the groups of detections in a database in association with the character ID assigned to each group.

Abstract: A system for indexing animated content receives detections extracted from a media file, where each one of the detections includes an image extracted from a corresponding frame of the media file that corresponds to a detected instance of an animated character. The system determines, for each of the received detections, an embedding defining a set of characteristics for the detected instance. The embedding associated with each detection is provided to a grouping engine that is configured to dynamically configure at least one grouping parameter based on a total number of the detections received. The grouping engine is also configured to sort the detections into groups using the grouping parameter and the embedding for each detection. A character ID is assigned to each one of the groups of detections, and the system indexes the groups of detections in a database in association with the character ID assigned to each group.

Abstract: A suspension superstructure adapted to be mounted to a dune buggy space frame, the space frame having a base frame and configured to support right and left dune buggy wheels at a first track width, the superstructure comprising: right and left frame brackets adapted to be coupled to the base frame; a bridging plate adapted to be connected to the right and left frame brackets; a bumper panel; and right and left wheel control arm supports mounted between the bridging plate and the bumper panel that support control arms of right and left dune buggy wheels so that the wheels have a second track width greater than the first track width.

Abstract: A suspension superstructure adapted to be mounted to a dune buggy space frame, the space frame having a base frame and configured to support right and left dune buggy wheels at a first track width, the superstructure comprising: right and left frame brackets adapted to be coupled to the base frame; a bridging plate adapted to be connected to the right and left frame brackets; a bumper panel; and right and left wheel control arm supports mounted between the bridging plate and the bumper panel that support control arms of right and left dune buggy wheels so that the wheels have a second track width greater than the first track width.