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Tracking semantic objects in vector image sequences
| Details |
Inventors: Gu, Chuang; Lee, Ming-Chieh;
Assignee: Microsoft Corporation (Redmond, WA)
Primary Examiner: Johnson; Timothy M.
Assistant Examiner: Choobin; Barry
Attorney, Agent or Firm: Klarquist Sparkman, LLP
A semantic object tracking method tracks general semantic objects with multiple non-rigid motion, disconnected components and multiple colors throughout a vector image sequence. The method accurately tracks these general semantic objects by spatially segmenting image regions from a current frame and then classifying these regions as to which semantic object they originated from in the previous frame. To classify each region, the method performs a region based motion estimation between each spatially segmented region and the previous frame to compute the position of a predicted region in the previous frame. The method then classifies each region in the current frame as being part of a semantic object based on which semantic object in the previous frame contains the most overlapping points of the predicted region. Using this method, each region in the current image is tracked to one semantic object from the previous frame, with no gaps or overlaps. The method propagates few or no errors because it projects regions into a frame where the semantic object boundaries are previously computed rather than trying to project and adjust a boundary in a frame where the object's boundary is unknown. |
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DETAILED DESCRIPTION The invention provides a method for tracking semantic objects in vector image sequences.
The invention is particularly well suited for tracking semantic video objects in digital video clips, but can also be used for a variety of other vector image sequences.
While the method is implemented in software program modules, it can also be implemented in digital hardware logic or in a combination of hardware and software components.
The method tracks semantic objects in an image sequence by segmenting regions from a frame and then projecting the segmented regions into a target frame where a semantic object boundary or boundaries are already known.
The projected regions are classified as forming part of a semantic object by determining the extent to which they overlap with a semantic object in the target frame.
For example, in a typical application, the tracking method repeats for each frame, classifying regions by projecting them into the previous frame in which the semantic object boundaries are previously computed.
The tracking method assumes that semantic objects are already identified in the initial frame.
To get the initial boundaries of a semantic object, a semantic object segmentation method may be used to identify the boundary of the semantic object in an initial frame.
After the initial frame, the tracking method operates on the segmentation results of the previous frame and the current and previous image frames.
For each frame in a sequence, a region extractor segments homogenous regions from the frame.
A motion estimator then performs region based matching for each of these regions to identify the most closely matching region of image values in the previous frame.
Using the motion parameters derived in this step, the segmented regions are projected into the previous frame where the semantic boundary is already computed.
A region classifier then classifies the regions as being part of semantic object in the current frame based on the extent to which the projected regions overlap semantic objects in the previous frame
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Image Analysis 
