Periodic Motion Detection in Video via Cepstral Projection
Solution Overview
Problem
Current methods for detecting periodic motion in multimedia content are inadequate in accurately distinguishing between periodic and non-periodic motions, particularly in complex video sequences, leading to inefficiencies in generating seamless animated images.
Innovation Solution
A method involving the selection of a region of interest (ROI) in a series of frames, determination of an object mobility data matrix, and projection of this matrix onto a line to identify periodic motion through cepstral analysis, allowing for the differentiation between periodic and non-periodic motions based on amplitude and quefrency in the cepstrum.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion detection methods are used, then the processing is simpler, but the accuracy in distinguishing periodic and non-periodic motions deteriorates
Solution Approach 1:
The patent segments the motion detection process into distinct stages: computing object mobility data matrix from frame differences, projecting the matrix onto a line to extract motion characteristics, and analyzing the projection to distinguish periodic from non-periodic motion. This segmentation allows each stage to be optimized independently, improving overall detection accuracy while managing complexity.
Solution Approach 2:
The patent introduces an intermediary representation - the object mobility data matrix and its projection - that transforms complex motion patterns into a form that is easier to analyze. The projection of the mobility matrix onto a line serves as an intermediary that captures essential periodicity characteristics while filtering out irrelevant information, enabling more accurate classification.
2Adaptability or versatility
If complex video sequences are processed, then more motion types can be detected, but the efficiency in generating animated images deteriorates
Solution Approach 1:
The patent extracts only the essential periodicity characteristics from complex video sequences by projecting the object mobility data matrix onto a line. This extraction process isolates the key features needed for animation generation while discarding redundant information, thereby maintaining versatility in detecting different motion types while improving processing efficiency.
Solution Approach 2:
The patent transforms the motion detection problem by changing parameters - converting spatial-temporal motion data into a projected representation that highlights periodicity. This parameter transformation enables the system to handle diverse motion types efficiently by focusing on the critical parameter of periodicity rather than processing all motion details equally.
Data Source
AI summary
In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating selection of a region of interest (ROI) in a plurality of frames of a multimedia content. The ROI is associated with a motion of at least one object. An object mobility data matrix associated with the ROI is determined in the plurality of frames. The object mobility data matrix is indicative of a difference in motion of the at least one object in the plurality of frames. A projection of the object mobility data matrix is determined on a line. The motion of the at least one object in the ROI is determined across the plurality of frames to as a periodic motion or a non-periodic motion based on the projection of the object mobility data matrix.