Hypergraph Transition Descriptor for Violent Incident Detection
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Solution Overview
Problem
The detection of violent incidents in videos is challenging due to their polymorphic nature, limited positive sample data, and low resolution of surveillance videos, making it difficult for deep learning models to effectively identify and classify such events.
Innovation Solution
A method using a hypergraph transition descriptor, specifically the Histogram of Velocity Changing (HVC), is employed to analyze the spatial relationship between feature points and transition conditions, combined with a Hidden Markov Model (HMM) to model the Hypergraph-Transition (H-T) Chain, which effectively captures the intensity and stability of movement and detects violent incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep learning models are used for violent incident detection, then automatic feature extraction and identification capability is improved, but the requirement for massive training data increases which cannot be met due to limited positive samples
Solution Approach 1:
The patent replaces expensive deep learning models requiring massive data with a lightweight local spatial-temporal feature descriptor method that can work effectively with limited surveillance video data, achieving comparable or better performance without needing large training datasets
Solution Approach 2:
The patent transforms the detection approach by changing from global deep learning features to local spatial-temporal feature descriptors, and further to hypergraph-based descriptors that capture motion patterns, enabling effective detection with limited data by focusing on specific motion characteristics rather than general features
2Quantity of substance
If local spatial-temporal feature descriptors are used for violent incident detection, then the requirement for training data is reduced, but the ability to capture complex motion patterns and behavior semantics is limited
Solution Approach 1:
The patent creates a composite feature descriptor that combines multiple types of information: local spatial-temporal features, hypergraph structure representing motion patterns, and velocity changing histograms. This composite approach captures both fine-grained local details and global motion semantics, overcoming the limitations of simple local descriptors
Solution Approach 2:
The patent introduces hypergraph-based descriptors that add a new dimension to feature representation by modeling the temporal evolution and spatial relationships of motion patterns across multiple frames, transforming 2D spatial features into 3D spatio-temporal hypergraph structures that better represent violent incident dynamics
3Device complexity
If traditional behavior recognition methods are used, then computational complexity is reduced, but detection accuracy for polymorphic violent incidents deteriorates
Solution Approach 1:
The patent segments the video analysis into multiple independent components: foreground detection, interest point extraction, hypergraph construction, and descriptor calculation. This segmentation allows each component to be optimized independently, achieving high accuracy through specialized processing while keeping overall computational complexity manageable through modular design
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting specific motion patterns characteristic of violent incidents rather than analyzing all possible behaviors. By using hypergraph descriptors that specifically capture aggressive motion dynamics, the method achieves high accuracy for violent incident detection without the computational burden of comprehensive behavior recognition
Data Source
AI summary
Provided is a method for detecting a violent incident in a video based on a hypergraph transition model, comprising a procedure of extracting a foreground target track, a procedure of establishing a hypergraph and a similarity measure, and a procedure of constructing a hypergraph transition descriptor; using the hypergraph to describe a spatial relationship of feature points, in order to reflect attitude information about a movement; and modelling the transition of correlative hypergraphs in a time sequence and extracting a feature descriptor HVC, wherein same can effectively reflect the intensity and stability of the movement. The method firstly analyses the spatial relationship of the feature points and a transition condition of a feature point group, and then performs conjoint analysis on same. The method of the present invention is sensitive to disorderly and irregular behaviours in a video, wherein same is applicable to the detection of violent incidents.