Static Object Detection via Dual Time-Span Background Modeling
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Solution Overview
Problem
Existing image processing systems face difficulties in accurately detecting static objects in videos with frequent changes, such as constant flows of people, due to challenges in generating appropriate background models.
Innovation Solution
An image processing system that identifies static areas in input images captured at multiple time points, generates background images using static areas from different time spans, and compares these images to detect areas with differences, effectively separating static and dynamic components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a long-term background model is used to detect static objects, then detection capability is improved, but the model becomes contaminated by frequent moving objects (flow of people) within the processing time scale
Solution Approach 1:
The patent divides the background modeling process into two distinct segments: a long-term background model for capturing stable background elements and a short-term background model for representing recent scene dynamics. By segmenting the time spans and comparing the two models, the system can identify static objects that persist across both time scales while filtering out transient moving objects that only appear in the short-term model.
Solution Approach 2:
The patent performs preliminary background modeling by establishing both long-term and short-term background models before conducting static object detection. This preliminary action allows the system to pre-characterize the background at different time scales, enabling subsequent comparison to reliably distinguish static objects from transient movements without contamination.
2Measurement precision
If motion analysis is performed at multiple time scales, then static object detection capability is improved, but difficulty in generating appropriate background models increases
Solution Approach 1:
The patent segments the background modeling task into two manageable components with distinct time spans. The long-term model captures stable background elements over an extended period, while the short-term model represents recent scene dynamics. This segmentation reduces the complexity of generating appropriate background models at each scale, making the overall process more tractable despite the multi-scale requirement.
Solution Approach 2:
The patent changes the temporal parameter (time span) to create distinct background models. By adjusting the time span parameter to generate both long-term and short-term models, the system can adapt to different detection needs without increasing the fundamental difficulty of background model generation. The comparison between models with different temporal parameters enables reliable static object detection.
Data Source
AI summary
An image processing system includes: a first identification unit that identifies a static area from an input image captured at each of a plurality of time points; an image generation unit that generates a first image by using the static areas of respective input images captured in a first time span from a processing time point and generates a second image by using the static areas of respective images captured in a second time span from the processing time point; and a second identification unit that compares the first image and the second image and identifies an area having a difference.