Motion History Image Object Analysis
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Solution Overview
Problem
Automated detection and tracking of moving objects in complex scenes are challenging due to factors like illumination changes, additional moving objects, and scene variations, leading to inefficient processing of irrelevant elements.
Innovation Solution
A method for object analysis using motion history, which involves processing video data to create a motion history image and identifying elements for further processing based on criteria satisfied by their motion history values, such as speed, duration, and direction, to differentiate relevant from irrelevant motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated detection and tracking processes analyze all moving elements in complex scenes, then comprehensive object detection is achieved, but processing efficiency deteriorates due to analyzing irrelevant elements like random motion
Solution Approach 1:
The patent segments the video data processing by creating separate motion history images for different temporal ranges (recent frames vs. older frames). This segmentation allows the system to apply different analysis criteria to different motion patterns, efficiently filtering out random short-term motions while tracking sustained meaningful movements.
Solution Approach 2:
The patent performs preliminary filtering by generating motion history images and applying motion criteria before conducting full object detection and tracking. This preliminary action identifies and eliminates irrelevant moving elements early in the processing pipeline, preventing wasted computation on inconsequential objects.
2Measurement precision
If motion analysis processes investigate all moving objects in detail, then accurate object tracking is achieved, but computational resources are wasted on irrelevant motion patterns
Solution Approach 1:
The system performs preliminary motion analysis by creating motion history images and evaluating motion criteria before committing to detailed tracking. This preliminary action consumes minimal processor time to filter out random motions, preserving computational resources for accurate tracking of only relevant objects.
Solution Approach 2:
The patent replaces intensive mechanical object-by-object analysis with a more efficient field-based approach using motion history images. This substitution allows parallel evaluation of motion patterns across the entire scene, reducing processor time while maintaining tracking accuracy for genuine objects.
3Adaptability or versatility
If the system processes video data without motion history filtering, then all objects are captured for analysis, but the complexity of scene elements increases making identification difficult
Solution Approach 1:
The patent segments motion history into different temporal layers (recent frames and older frames), allowing the system to maintain comprehensive scene coverage while organizing complex motion data into manageable segments. This segmentation reduces analysis complexity by enabling targeted processing of different motion types.
Solution Approach 2:
The motion history image serves as an intermediary representation between raw video data and final object identification. This intermediary structure simplifies complex scene analysis by encoding motion patterns in a standardized format that facilitates systematic filtering and categorization of scene elements.
Data Source
AI summary
A method for object analysis using motion history is provided. The method includes receiving video data comprising a plurality of frames of a scene comprising one or more elements, and processing the video data to produce a motion history image comprising motion history values of at least one of the elements. The method also includes identifying the one of the elements for further processing if a characteristic of the motion history image satisfies a criteria.