Shot Transition Detection With AI Verification and Dynamic Thresholds
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Solution Overview
Problem
Existing video analysis methods struggle to efficiently detect both cut and gradual transitions between shots in videos, with AI-based detectors consuming excessive computational resources and traditional detectors lacking accuracy, particularly for gradual transitions.
Innovation Solution
A multistage shot detection technique using a traditional and AI model-based transition detector in combination, with dynamic thresholds to optimize computational efficiency and accuracy, where the traditional detector identifies candidate transitions and the AI detector verifies these candidates, further refined by multiple thresholds to distinguish between cut and gradual transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an AI-based shot transition detector is used to improve detection accuracy, then detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The video processing task is segmented into two stages: first, a traditional detector identifies candidate transitions at a lower computational cost; second, the AI-based detector is applied only to these candidates for verification. This segmentation allows the system to achieve high detection accuracy while minimizing overall computational resource consumption by avoiding exhaustive AI analysis of all video frames.
Solution Approach 2:
The traditional shot transition detector performs preliminary action by identifying and filtering candidate transitions before the AI-based detector processes them. This preliminary filtering reduces the input size for the computationally intensive AI model, thereby maintaining high detection accuracy while significantly reducing total computational resource requirements.
2Productivity
If a traditional shot transition detector is used to reduce computational resource consumption, then computational efficiency is improved, but detection accuracy deteriorates
Solution Approach 1:
The detection system is segmented into two functional components: a traditional detector for initial candidate identification (prioritizing computational efficiency) and an AI-based detector for candidate verification (prioritizing detection accuracy). This segmentation allows each component to operate in its optimal performance regime.
Solution Approach 2:
The traditional detector acts as an intermediary that bridges the gap between computational efficiency and detection accuracy. It filters and prepares candidate transitions, which are then passed to the AI-based detector for final verification, thereby enabling the system to achieve both computational efficiency and high detection accuracy.
3Device complexity
If a single threshold is used to simplify the detection process, then device complexity is reduced, but the ability to distinguish between cut and gradual transitions deteriorates
Solution Approach 1:
The system employs dynamic, adaptive thresholds instead of fixed thresholds. The thresholds are adjusted based on the specific video content and transition characteristics, allowing the system to accurately distinguish between cut and gradual transitions while adapting to different video scenarios. This dynamic approach maintains relatively simple device complexity while significantly improving transition type differentiation accuracy.
Solution Approach 2:
The detection system changes the parameter of threshold values dynamically based on video content analysis. By adjusting threshold parameters according to the specific characteristics of each video and transition type, the system achieves accurate differentiation between cut and gradual transitions without requiring complex device architecture.
Data Source
AI summary
Systems and methods for providing efficient shot transition detection for shot segmentation of a video. A traditional shot transition detector and a neural network shot transition detector are used in multiple stages to identify transitions between shots in the video. Further, dynamic thresholds are determined based on visual attributes of the video that are used to detect cut transitions and gradual transitions.