Left-Behind Object Detection via Temporal Background Segmentation
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Solution Overview
Problem
Conventional two-class segmentation methods fail to effectively detect objects left behind in a scene, as they require object tracking, which is difficult in complex real-world scenarios, and cannot differentiate between static background, active foreground, and left-behind objects.
Innovation Solution
A method that segments a sequence of images into background and foreground portions without requiring a model of the scene or object, using temporal aspects of pixel changes to differentiate between static background, active foreground, and left-behind objects by constructing long-term and short-term background models as mixtures of Gaussian distributions and employing a Bayesian update mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional two-class segmentation methods are used, then the segmentation process is simple, but the method cannot differentiate between static background, active foreground, and left-behind objects
Solution Approach 1:
The patent divides the segmentation task into three distinct classes: static background, active foreground, and left-behind objects. This is achieved by analyzing temporal characteristics of pixel changes across multiple frames, where static background shows minimal change, active foreground shows rapid change, and left-behind objects show intermediate change patterns. The method segments the image sequence into these three categories using temporal analysis rather than treating it as a simple two-class problem.
2Measurement precision
If object tracking is used to detect left-behind objects, then detection capability is improved, but the processing complexity and difficulty increase significantly in complex real-world scenarios
Solution Approach 1:
The patent extracts and analyzes the temporal characteristics of pixel changes independently from full object tracking. Instead of tracking entire objects through complex algorithms, the method extracts temporal signatures of pixel intensity changes and uses these to identify left-behind objects. This extraction approach simplifies the problem by focusing only on the temporal dimension of pixel data rather than requiring complete object detection and tracking pipelines.
Solution Approach 2:
The method dynamically adapts to different scene conditions by analyzing temporal patterns of pixel changes. It uses dynamic thresholding and adaptive parameters that adjust based on the observed motion patterns in the scene. The system dynamically distinguishes between different types of motion (background changes, foreground motion, and stationary objects) by continuously analyzing temporal derivatives of pixel values.
3Adaptability or versatility
If background models are updated iteratively for each frame, then the model adapts to scene changes, but the computational cost grows exponentially as the number of models increases
Solution Approach 1:
Instead of maintaining and updating comprehensive background models for every possible scene configuration, the patent uses a simplified approach that updates only the necessary temporal statistics. It maintains compact representations of background characteristics and updates only the specific temporal parameters that are relevant to detecting left-behind objects. This partial action approach avoids the exponential growth of computational complexity while maintaining adequate adaptation capability.
4Adaptability or versatility
If non-parametric kernel density estimation is used to store color values of all pixels, then the method handles arbitrary distributions, but both memory and computational cost are proportional to the number of images making it impractical for real-time applications
Solution Approach 1:
The patent applies local quality by analyzing temporal characteristics at the pixel level rather than maintaining global models for all pixels. It processes each pixel's temporal signature independently and uses localized temporal statistics to classify pixels into background, foreground, or left-behind categories. This local approach reduces memory requirements from storing all pixel values across all images to storing only compact temporal parameters for each pixel position.
Data Source
AI summary
A method detects an object left-behind in a scene by updating a set of background models using a sequence of images acquired of the scene by a camera. Each background model is updated at a different temporal scales ranging from short term to long term. A foreground mask is determined from each background model after the updating for a particular image of the sequence. A motion image is updated from the set of foreground masks. In the motion, image, each pixel has an associated evidence value. The evidence values are compared with a evidence threshold to detect and signal an object left behind in the scene.