Abstract: An electronic device for object tracking based on a user-specified initialization point is provided. The electronic device stores a sequence of image frames, which includes a first image frame and a second image frame. The electronic device estimates a set of feature correspondences between a first set of features points in the first image frame and a second set of feature points in the second image frame. The electronic device generates different first motion-estimate models for different groups of feature correspondences of the set of feature correspondences and further estimates, from different groups of feature correspondences, a plurality of inlier feature correspondences that correspond to the object of interest in the first image frame and the second image frame. The electronic device generates a second motion-estimate model as an optimal motion-estimate model and tracks the object of interest in the sequence of frames, based on the second motion-estimate model.

Abstract: An electronic device for object tracking based on a user-specified initialization point is provided. The electronic device stores a sequence of image frames, which includes a first image frame and a second image frame. The electronic device estimates a set of feature correspondences between a first set of features points in the first image frame and a second set of feature points in the second image frame. The electronic device generates different first motion-estimate models for different groups of feature correspondences of the set of feature correspondences and further estimates, from different groups of feature correspondences, a plurality of inlier feature correspondences that correspond to the object of interest in the first image frame and the second image frame. The electronic device generates a second motion-estimate model as an optimal motion-estimate model and tracks the object of interest in the sequence of frames, based on the second motion-estimate model.

Abstract: Various aspects of a video processing system and method for object tracking in video content are disclosed herein. The system includes one or more circuits in an electronic device that are configured to detect a current state of an object in a current image frame from a sequence of image frames based on a plurality of first parameters. The system is further configured to adjust a plurality of second parameters for computation of a plurality of features of the object in the current image frame, based on the detected current state. The system is further configured to track the object in the current image frame based on a first feature of the computed plurality of features of the object.

Abstract: Various aspects of a video processing system and method are provided for object tracking in a sequence of image frames are disclosed herein. The video processing system includes one or more circuits in an electronic device that acquires the sequence of image frames, which comprises at least a current image frame and a next image frame including an object. A distribution of first pixel values in a first image space associated with a first region, which corresponds to the object in the current image frame, is determined. Based on the distribution, the current image frame is transformed to a modified current image frame that includes at least a second region that corresponds to the first region and is associated with second pixel values in a second image space. Based on one or more features extracted as a template associated with the second region, the object is tracked in the next image frame.

Abstract: Various aspects of a video processing system and method for object tracking in video content are disclosed herein. The system includes one or more circuits in an electronic device that are configured to detect a current state of an object in a current image frame from a sequence of image frames based on a plurality of first parameters. The system is further configured to adjust a plurality of second parameters for computation of a plurality of features of the object in the current image frame, based on the detected current state. The system is further configured to track the object in the current image frame based on a first feature of the computed plurality of features of the object.

Abstract: Various aspects of a video processing system and method are provided for object tracking in a sequence of image frames are disclosed herein. The video processing system includes one or more circuits in an electronic device that acquires the sequence of image frames, which comprises at least a current image frame and a next image frame including an object. A distribution of first pixel values in a first image space associated with a first region, which corresponds to the object in the current image frame, is determined. Based on the distribution, the current image frame is transformed to a modified current image frame that includes at least a second region that corresponds to the first region and is associated with second pixel values in a second image space. Based on one or more features extracted as a template associated with the second region, the object is tracked in the next image frame.

Abstract: Various aspects of a method and system to track one or more objects in a video stream are disclosed herein. In accordance with an embodiment, the method includes computation of a first confidence score of a first geometrical shape that encompasses at least a portion of an object in a first image frame of the video stream. The first geometrical shape is utilized to track the object in the video stream. The first geometrical shape is split into a plurality of second geometrical shapes. The split of the first geometrical shape is based on a comparison of the computed first confidence score with a pre-defined threshold score.

Abstract: Various aspects of a method and system for object tracking are disclosed herein. The method comprises computation of an overlap value between a first bounding box and a second bounding box, associated with an object in an image frame of a video stream. The overlap values are utilized to track the object in the video stream. The first bounding box encompasses at least a partial first portion of an object. The second bounding box encompasses at least a partial second portion of the object. A first score associated with the first bounding box and a second score associated with the second bounding box is computed based on a comparison of the computed overlap value with a pre-defined threshold value. The first bounding box is updated to a new bounding box that at least partially encompasses the object, based on the computed first score and the computed second score.

Abstract: Various aspects of a method and system for object tracking are disclosed herein. The method comprises computation of an overlap value between a first bounding box and a second bounding box, associated with an object in an image frame of a video stream. The overlap values are utilized to track the object in the video stream. The first bounding box encompasses at least a partial first portion of an object. The second bounding box encompasses at least a partial second portion of the object. A first score associated with the first bounding box and a second score associated with the second bounding box is computed based on a comparison of the computed overlap value with a pre-defined threshold value. The first bounding box is updated to a new bounding box that at least partially encompasses the object, based on the computed first score and the computed second score.

Abstract: Various aspects of a method and system to track one or more objects in a video stream are disclosed herein. In accordance with an embodiment, the method includes computation of a first confidence score of a first geometrical shape that encompasses at least a portion of an object in a first image frame of the video stream. The first geometrical shape is utilized to track the object in the video stream. The first geometrical shape is split into a plurality of second geometrical shapes. The split of the first geometrical shape is based on a comparison of the computed first confidence score with a pre-defined threshold score.

Abstract: The invention relates to a method for estimating an image relation model for two or more related images based on a plurality of point correspondences. The method includes using a weighted random sampling algorithm to repeatedly draw subsets of point correspondences for generating model estimates, evaluating said model estimates for the full plurality of the point correspondences, and updating weights associated with each of the point correspondences in the random sampling algorithm based on information obtained in said evaluating.

Abstract: The invention relates to a method for estimating an image relation model for two or more related images based on a plurality of point correspondences. The method includes using a weighted random sampling algorithm to repeatedly draw subsets of point correspondences for generating model estimates, evaluating said model estimates for the full plurality of the point correspondences, and updating weights associated with each of the point correspondences in the random sampling algorithm based on information obtained in said evaluating.