Abstract: The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.

Abstract: The motion of objects within a scene may be detected and tracked using digital (e.g., visual and depth) cameras aligned with fields of view that overlap at least in part. Objects may be identified within visual images captured from the scene using a tracking algorithm and correlated to point clouds or other depth models generated based on depth images captured from the scene. Once visual aspects (e.g., colors or other features) of objects are correlated to the point clouds, shapes and/or positions of the objects may be determined and used to further train the tracking algorithms to recognize the objects in subsequently captured frames. Moreover, a Kalman filter or other motion modeling technique may be used to enhance the prediction of a location of an object within subsequently captured frames.

Abstract: A system and method for detecting a transition in a stream of images of a gastrointestinal (GI) tract may include selecting images from an in-vivo image stream; calculating a segment score for each selected image indicating in which segment of the GI tract the image was captured; applying a smoothing function on the scores; detecting a global step in the smoothed segment score signal indicating a substantial change in a parameter calculated based on segment score signal values of the segment score signal values; detecting a local step indicating a substantial change in a parameter calculated based on segment score signal values of a predetermined interval of the of the segment score signal values; combining the local step and the global step; and determining a point of transition in the stream from one anatomical segment to another, the point of transition correlating to the combined step.