Abstract: Techniques disclosed for managing video captured by an imaging device. Methods disclosed capture a video in response to a capture command received at the imaging device. Following a video capture, techniques for classifying the captured video based on feature(s) extracted therefrom, for marking the captured video based on the classification, and for generating a media item from the captured video according to the marking are disclosed. Accordingly, the captured video may be classified as representing a static event, and, as a result, a media item of a still image may be generated. Otherwise, the captured video may be classified as representing a dynamic event, and, as a result, a media item of a video may be generated.

Abstract: Techniques disclosed for managing video captured by an imaging device. Methods disclosed capture a video in response to a capture command received at the imaging device. Following a video capture, techniques for classifying the captured video based on feature(s) extracted therefrom, for marking the captured video based on the classification, and for generating a media item from the captured video according to the marking are disclosed. Accordingly, the captured video may be classified as representing a static event, and, as a result, a media item of a still image may be generated. Otherwise, the captured video may be classified as representing a dynamic event, and, as a result, a media item of a video may be generated.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.