Foreground Background Image Processing for Still Object Detection
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Solution Overview
Problem
Current background filtering methods fail to accurately extract long-time-still objects from video sequences as they are often misclassified as background due to limited differences between frames, leading to incomplete object extraction.
Innovation Solution
A system and method for image processing that involves determining the presence of an object of interest and updating background images based on frames with no objects, using multiple background images corresponding to different aspects of a scene to improve foreground detection, particularly for long-time-still objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional background filtering methods are used to process video frames, then processing speed is maintained, but long-time-still objects are misclassified as background and fail to be extracted accurately
Solution Approach 1:
The system performs preliminary object detection on video frames before background filtering to identify frames containing objects of interest. This preliminary action allows the system to selectively apply background filtering only to frames without objects, preventing long-time-still objects from being misclassified as background while maintaining efficient processing by avoiding unnecessary filtering on all frames.
Solution Approach 2:
The system dynamically adjusts the background filtering process based on the detection results. When objects are detected in a frame, the background filtering is modified or skipped for that frame, allowing the system to adapt its processing strategy in real-time to preserve object information while maintaining overall processing efficiency.
2Measurement precision
If background images are updated frequently to capture scene changes, then detection accuracy improves, but system complexity and computational load increase
Solution Approach 1:
The system performs preliminary detection to identify frames without objects before updating background images. This preliminary action ensures that only appropriate frames are used for background updates, improving detection accuracy while avoiding the complexity of processing and managing excessive background image data from frames containing objects.
Solution Approach 2:
The system automatically determines which frames are suitable for background image updates based on object detection results, eliminating the need for manual intervention or complex external control systems. This self-service approach simplifies the overall system architecture while maintaining high detection accuracy through intelligent, context-aware background updates.
Data Source
AI summary
A method and system for image processing are provided in the present disclosure. The method may include obtaining a first image and determining whether there is an object of interest in the first image by performing an object detection on the first image. The method may also include upon a determination that there is an object of interest in the first image, retrieving at least one background image from a first storage device, and obtaining a foreground image. The method may further include upon a determination that there is no object of interest in the first image, determining whether a first predetermined condition is satisfied; and upon a determination that the first predetermined condition is satisfied, updating the at least one background image in the first storage device based on at least one candidate image determined as including no object of interest.