Video Frame Masking Using Color Space Classification
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Solution Overview
Problem
Existing video surveillance methods face challenges in accurately detecting and masking facial features across varying skin tones and lighting conditions, and are inefficient in handling changes in camera view or illumination, leading to incomplete masking and high computational demands.
Innovation Solution
A method that treats each video frame independently, using color and motion-based techniques to classify objects within a defined color space, such as YCbCr, and applies a mask to obscure identifying features without object tracking, allowing for manual mask modification to improve accuracy and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial feature detection and tracking is used to mask identifying features, then masking accuracy is improved, but computational demand increases significantly
Solution Approach 1:
The patent extracts only the essential characteristics needed for masking (colour and motion information) rather than performing complete facial feature detection and tracking. By taking out only the necessary elements (colour space classification and motion detection) and discarding complex facial recognition processing, the system achieves adequate masking accuracy with significantly reduced computational demand.
2Speed
If colour-based detection is used to identify facial features, then detection speed is improved, but accuracy decreases due to variations in skin tone and lighting conditions
Solution Approach 1:
The patent merges colour-based detection with motion-based detection to overcome the limitations of using colour alone. By combining these two detection methods, the system maintains fast detection speed while improving accuracy through multiple verification criteria (colour classification in YCbCr space plus motion detection), thereby compensating for variations in skin tone and lighting conditions.
3Productivity
If background subtraction is used to separate moving objects, then motion detection capability is improved, but adaptability to lighting changes deteriorates
Solution Approach 1:
The patent performs preliminary colour classification of pixels before applying background subtraction. By pre-classifying pixels into skin-tone and non-skin-tone categories using YCbCr colour space, the system prepares the data in advance so that subsequent motion detection and background subtraction can focus only on relevant regions, improving both motion detection capability and robustness to lighting changes.
4Reliability
If object tracking is implemented to follow moving targets across frames, then continuous masking is improved, but system complexity increases
Solution Approach 1:
The patent segments the video processing into independent frame-by-frame operations rather than implementing continuous object tracking across frames. Each frame is processed independently through colour classification and motion detection, eliminating the need for complex tracking algorithms while maintaining reliable masking through consistent per-frame analysis.
Data Source
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AI summary
A method of processing a video comprising a sequence of image frames comprises a background modelling step, in which motion information is extracted from a series of images of a scene and any remaining information is summed to create a background model that represents static components of the scene, a foreground extraction step, in which the background model is subtracted from at least one image frame to extract a foreground scene that represents moving components of a scene, and a colour classification step, in which objects within the foreground scene are classified according to whether each of those objects falls inside or outside a defined colour space.