Sparse Feature Map Generation for Surveillance Scene Analysis
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Solution Overview
Problem
Video surveillance systems face high computational resource demands due to the need for manual analysis of large amounts of video data, leading to labor costs and inefficiencies, particularly in processing static backgrounds and identifying dynamic events.
Innovation Solution
An image processing method that captures changes in a monitored scene and performs sparse feature calculation to obtain a sparse feature map, utilizing event cameras or frame difference processing to focus on dynamic areas, reducing unnecessary data processing and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis is used to monitor large amounts of video data, then comprehensive monitoring coverage is achieved, but labor costs increase and inspection efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated image processing system that uses event cameras and sparse convolution neural networks. The system automatically captures changes in monitored scenes, performs sparse feature calculations, and generates feature maps without human intervention, thereby eliminating labor costs while improving inspection efficiency through continuous automated operation.
2Measurement precision
If traditional convolution neural network is used to process surveillance video, then comprehensive feature extraction is achieved, but computing resource consumption becomes massive
Solution Approach 1:
The patent extracts only the essential dynamic information from video frames by using event cameras to capture changes and applying sparse convolution operations. Instead of processing entire frames, the system extracts and processes only the sparse regions containing actual changes, significantly reducing computing resource consumption while maintaining feature extraction accuracy for dynamic events.
Solution Approach 2:
The patent applies different processing strategies to different regions of the video data. Event cameras and sparse convolution operations focus computational resources only on regions where changes occur, rather than uniformly processing the entire frame. This localised approach maintains high feature extraction accuracy for dynamic areas while minimizing unnecessary computation in static regions.
3Use of energy by moving object
If block-based sparse convolution is used, then some computational reduction is achieved, but overlapping regions create additional computational overhead and blocks with large sparseness are still processed
Solution Approach 1:
The patent performs preliminary processing by using event cameras to capture changes before convolution operations. This preliminary capture of dynamic information allows the system to identify sparse regions in advance, avoiding the need to process entire blocks and eliminating computational overhead from overlapping regions. The event-based approach pre-filters the data to contain only meaningful changes.
Data Source
AI summary
An image processing method including: capturing changes in a monitored scene; and performing a sparse feature calculation on the changes in the monitored scene to obtain a sparse feature map.