Video Signal Analysis for Crowded Surveillance Scene Changes
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Solution Overview
Problem
Current surveillance video analysis technologies fail to effectively detect meaningful scene changes in crowded and complex scenes with unclear semantics, as they rely on object-based or motion-based approaches that assume well-defined semantics and clear object segmentation, which are not applicable in real-world scenarios with dynamic and occluded environments.
Innovation Solution
A dynamic visual scene analysis method that uses intermediate-level analysis, combining local area change information and low-level motion features to detect scene changes through temporal segmentation and motion activity analysis, without relying on explicit object tracking or prior knowledge, and adapts to scenes with varying semantics and occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object-based analysis is used for surveillance scene change detection, then detection accuracy is improved in simple scenes with clear semantics, but system performance degrades in crowded or complex scenes
Solution Approach 1:
The patent introduces an intermediate-level representation that bridges pixel-level data and object-level semantics. This intermediate representation captures motion patterns and temporal changes without requiring full object segmentation, enabling effective analysis in crowded scenes where traditional object-based methods fail.
Solution Approach 2:
The patent segments the video analysis process into multiple levels: pixel-level change detection, intermediate-level motion pattern recognition, and semantic-level interpretation. This multi-level segmentation allows the system to handle complex scenes by processing information at appropriate granularities without being overwhelmed by scene complexity.
2Ease of manufacture
If traditional image processing algorithms such as background subtraction and blob tracking are used, then implementation simplicity is maintained, but effectiveness is lost in analyzing crowded scenes with occlusions
Solution Approach 1:
The patent employs dynamic temporal modeling that adapts to changing scene conditions. The intermediate-level representation continuously updates motion patterns over time, allowing the system to maintain reliability in dynamic environments with occlusions and varying scene configurations without requiring complex reconfiguration.
3Measurement precision
If 3D information is used to disambiguate occlusions, then object tracking accuracy is improved, but system complexity increases significantly
Solution Approach 1:
The patent applies partial 3D information selectively rather than comprehensively. The intermediate-level representation incorporates depth cues and spatial relationships only where necessary to resolve specific ambiguities, achieving improved tracking accuracy without the full computational burden of complete 3D reconstruction and processing.
4Ease of operation
If content-based representation in terms of video objects is used, then visual information management is improved, but the representation fails when cameras are not favorably positioned or scenes are crowded
Solution Approach 1:
The patent introduces an intermediate-level representation that serves as a mediator between raw pixel data and high-level object semantics. This intermediate representation maintains visual information manageability through structured motion patterns while being robust to unfavorable camera positions and crowded scenes, as it does not depend on clear object boundaries or optimal viewing angles.
Data Source
AI summary
A video signal is analysed by deriving for each frame a plurality of parameters, said parameters including (a) at least one parameter that is a function of the difference between picture elements of that frame and correspondingly positioned picture elements of a reference frame; (b) at least one parameter that is a function of the difference between picture elements of that frame and correspondingly positioned picture elements of a previous frame; and (c) at least one parameter that is a function of the difference between estimated velocities of picture elements of that frame and the estimated velocities of the correspondingly positioned elements of an earlier frame. Based on these parameters, each frame is assigned one or more of a plurality of predetermined classifications. Scene changes may then be identified as points at which changes occur in these classification assignments.