Action Keyframe Generation for Video Activity Visualization
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Solution Overview
Problem
Existing video analysis methods fail to effectively present activity in video segments for fixed-position cameras, particularly in separating foreground and background objects, handling noise, and visualizing motion due to limitations in bandwidth and computational complexity, especially in varying lighting conditions and with objects at rest or in motion.
Innovation Solution
A method that separates background from foreground objects in a video segment to create a single still image by alpha-blending translucent images of moving objects with the average background, varying opacity and color to indicate importance and motion, and subdividing the image when necessary to avoid clutter, using optimal sampling rates and alpha masking techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video frames are averaged to create a single image, then bandwidth requirements are reduced, but foreground objects become faintly visible and are dominated by the bright background
Solution Approach 1:
The patent extracts foreground objects from the video frames by detecting changes between frames. Pixels that change between consecutive frames are identified as foreground, while static pixels are classified as background. This separation allows the foreground objects to be visualized clearly without being dominated by the bright background, resolving the contradiction between bandwidth reduction and foreground visibility.
2Measurement precision
If subsequent frames are compared to identify changed pixels, then moving objects can be detected, but objects at rest cannot be found and the approach is very susceptible to video noise
Solution Approach 1:
The patent performs preliminary actions by maintaining a buffer of previous frames and pre-processing them to identify background pixels before comparing with current frames. This allows objects at rest to be identified as background through temporal consistency, while moving objects are detected as changes. The multi-frame comparison approach also helps filter out random noise by requiring consistent change patterns across multiple frames.
3Measurement precision
If all previous pixel values are stored in memory to determine the median, then accurate background separation is achieved, but prohibitively large amounts of memory are required for longer intervals
Solution Approach 1:
The patent uses a buffer window approach that retains only recent frames in memory rather than storing all historical pixel values. The buffer window maintains a limited number of recent frames sufficient for accurate median calculation and background separation, discarding older frames that are no longer relevant. This provides a practical balance between memory constraints and separation accuracy.
4Loss of energy
If a single still image is created from video frames, then bandwidth requirements are reduced, but clutter occurs when multiple objects move or movement occurs around much of the field of view
Solution Approach 1:
The patent segments the video content by creating multiple keyframes, each representing a specific time interval or aspect of the video. Instead of combining all activity into a single cluttered image, the segmentation distributes different temporal or spatial information across multiple keyframes, maintaining visual clarity while still reducing bandwidth requirements compared to transmitting the full video stream.
Data Source
AI summary
Techniques for generating action keyframes for a fixed-position camera based on the identification of activity in the video, an assessment of the importance of the activity, object recognition in the video, and interaction techniques for seeing more details of the video are presented. In different embodiments of the invention, the importance of activity is determined based on the amount of activity, important locations in the video streams, detected features such as faces, and events from other sensors.