Video Frame Analysis for Targeted Browsing
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Solution Overview
Problem
Browsing large volumes of videos to locate specific content is time-consuming and computationally intensive, especially when done frame-by-frame, leading to imprecise and unreliable outcomes, which is unacceptable for use cases like surveillance or web conference recordings.
Innovation Solution
A content engine computes dissimilarity metrics between successive key frames in a video, allowing for targeted browsing by skipping portions with below-threshold metrics, using techniques like foreground segmentation and similarity metrics such as Minkowski or Euclidean distance to determine playback speed and display visual representations of these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame-by-frame video browsing is performed, then detailed content inspection is achieved, but time consumption and computational load increase significantly
Solution Approach 1:
The video is segmented into key frames and non-key frames based on dissimilarity metrics. Only key frames with significant content changes are selected for detailed inspection, while non-key frames are skipped or browsed at reduced resolution. This segmentation allows precise content inspection at critical moments while reducing overall browsing time.
Solution Approach 2:
The system computes dissimilarity metrics between successive frames and skips frames with below-threshold metrics that contain redundant or unchanged content. By rushing through these non-informative frames and only stopping for detailed inspection at key frames with significant changes, the system achieves both time efficiency and content inspection accuracy.
2Reliability
If all video frames are processed to ensure accurate content identification, then reliability is improved, but computational intensity increases
Solution Approach 1:
The system applies different processing quality levels to different parts of the video based on local content characteristics. Frames with high dissimilarity metrics (key frames) receive full processing and detailed analysis, while frames with low dissimilarity metrics (non-key frames) receive minimal or no processing. This local quality approach ensures reliable content identification at critical points while reducing overall computational load.
Solution Approach 2:
Instead of processing all frames with equal intensity, the system performs partial action by selectively processing only those frames that contribute significantly to content identification. The dissimilarity metric computation identifies which frames require full processing versus which can be partially processed or skipped, achieving reliable content identification with reduced computational effort.
3Measurement precision
If dissimilarity metrics are computed between all successive frames, then content changes are accurately detected, but data processing redundancy increases
Solution Approach 1:
The system extracts and processes only the essential information needed for content change detection by computing dissimilarity metrics selectively. Instead of processing all frame data equally, it extracts key features and computes metrics only where necessary to detect meaningful content changes, removing redundant processing of unchanged or minimally changed frames.
Data Source
AI summary
A method for video frame analysis includes determining a first dissimilarity metric and a second dissimilarity metric. The first dissimilarity metric may correspond to a first difference between a first foreground of a first key frame in a video and a second foreground of a second key frame following the first key frame in the video. The second dissimilarity metric may correspond to a second difference between the second foreground of the second key frame and a third foreground of a third key frame following the second key frame in the video. A playback of the video may be generated based on the first dissimilarity metric and the second dissimilarity metric. Related systems and computer program products are also provided.