Motion Vector Field Orientation Binning for Video Saliency Detection
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Solution Overview
Problem
Current methods for identifying salient features in video sequences, such as motion activity climax or motion activity saliency, are cumbersome and time-consuming, particularly in video hosting and surveillance applications, where manual selection of thumbnails or reviewing large amounts of video for incident sections is required.
Innovation Solution
A system that analyzes streaming or stored video by generating a motion vector field, partitioning it into grid blocks, and calculating a motion score based on the count of motion vectors in orientation ranges to identify sections with higher motion, which are designated as salient features for use in thumbnail images, highlight reels, or video editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of thumbnails or review of video sections is performed, then accurate identification of salient features is achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that generates motion vectors, calculates motion scores, and identifies salient features algorithmically. The system processes video frames computationally to detect motion patterns and automatically select thumbnails without human intervention, thereby eliminating time consumption while maintaining identification accuracy.
Solution Approach 2:
The system enables the video processing system to automatically identify and select salient features without requiring manual human review. The automated motion analysis and scoring system serves itself to generate thumbnails and identify important video sections, replacing the need for human operators to manually review and select frames.
2Reliability
If comprehensive video review is conducted to locate incident sections, then complete coverage of important events is ensured, but productivity and processing efficiency decrease
Solution Approach 1:
The patent replaces comprehensive manual video review with automated motion-based detection systems that analyze motion vectors and scores across all video frames. This computational approach ensures complete coverage of incident sections by systematically processing entire video sequences while dramatically improving processing efficiency through algorithmic automation.
Solution Approach 2:
The system performs preliminary motion analysis on entire video sequences before final incident detection. By pre-calculating motion vectors and scores for all frames, the system prepares data structures that enable rapid identification of incident sections without requiring subsequent comprehensive manual review, thus maintaining coverage completeness while boosting productivity.
3Measurement precision
If detailed motion analysis with grid blocks and orientation ranges is performed, then precision in salient feature detection is improved, but computational complexity increases
Solution Approach 1:
The patent divides the video frame into grid blocks and further segments motion vectors into orientation ranges within each block. This hierarchical segmentation allows precise localization of motion patterns in both spatial and directional domains while managing computational complexity through structured data organization and progressive analysis levels.
Solution Approach 2:
The system applies different analysis granularities to different regions of the video frame through grid block segmentation. Each block can be analyzed with appropriate detail based on its local motion characteristics, allowing high detection precision in regions with significant motion while reducing unnecessary computational complexity in static regions.
Data Source
AI summary
A streaming video or a stored video is analyzed to identify salient features. The salient features are the more interesting portions of the video because salient features include the most motion. A motion vector field including a motion vector for multiple pixels in the video is generated. The motion vector field is partitioned into grid blocks, and each of the grid blocks is divided into a set of orientation ranges. The vectors of the motion vector field for each grid block are binned into the orientation ranges. A motion score is calculated based on a count of motion vectors for the orientation ranges. The section of the video having higher or the highest motion score is designated as a salient feature. Among other applications, salient features may be used for thumbnail images, highlight reels, or video editing.