Unsupervised Static Content Identification for Video Segmentation
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Solution Overview
Problem
Conventional techniques for identifying channel logos in video content require extensive manual effort and are ineffective in handling dynamic logo changes, making it difficult to accurately separate program and advertisement segments in broadcast television.
Innovation Solution
An unsupervised approach that analyzes video content in two passes to extract reference templates of static content from mean images, allowing for the identification of program and advertisement segments without pre-existing templates, using methods like bilateral filtering, adaptive thresholding, and edge detection to refine and match templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to identify channel logos in video content, then manual effort is required, but the process becomes time-consuming and ineffective for dynamic logo changes
Solution Approach 1:
The system automatically extracts reference templates of channel logos from the video content itself through unsupervised analysis, eliminating the need for manual template creation. The two-pass approach enables the system to self-identify logos and segments without human intervention, resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The first pass performs preliminary extraction of reference templates from mean images before the actual identification process in the second pass. This preliminary action prepares the necessary reference data in advance, making the subsequent logo identification faster and more accurate without requiring manual effort during the actual processing.
2Adaptability or versatility
If pre-existing templates are used for segment identification, then the process is simple, but it cannot handle dynamic logo changes
Solution Approach 1:
The system dynamically extracts reference templates from the actual video content rather than relying on static pre-existing templates. This dynamic approach allows the system to adapt to logo changes automatically, as the templates are regenerated from the mean images of the video content itself, capturing the current state of channel logos.
Solution Approach 2:
The system extracts reference templates by removing the program content and isolating the static channel logo overlays from the video frames. This extraction process separates the dynamic logo elements from the varying program content, enabling the system to handle logo changes while managing complexity through automated image processing techniques.
3Productivity
If manual methods are used to separate program and advertisement segments, then accuracy can be maintained, but productivity decreases
Solution Approach 1:
The system replaces manual mechanical segmentation processes with automated computer vision techniques. The two-pass approach uses image processing algorithms to automatically identify and label program and advertisement segments based on the presence of extracted reference templates, achieving both high productivity and accurate segment separation without manual intervention.
Data Source
AI summary
In one aspect, an example method includes (i) determining, by a computing system, a mean image of a set of frames of video content; (ii) extracting, by the computing system, a reference template of static content from the mean image; (iii) identifying, by the computing system, the extracted reference template of static content in a frame of the set of frames of the video content; (iv) labeling a segment within the video content as either a program segment or an advertisement segment based on the identifying of the extracted reference template of static content in the frame of the video content; and (v) generating data identifying the labeled segment.