Dynamic Weather Detection Using 3D Image Patch Segmentation
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Solution Overview
Problem
Conventional methods for detecting dynamic weather events like rain, snow, or hail using CCTV surveillance cameras face challenges with low-resolution cameras, high dynamic environments, and cluttered scenes, relying on visible rain streaks and assuming high frame rates, which limits their effectiveness.
Innovation Solution
The method involves receiving video images, dividing them into regions based on distance from the camera, segmenting into 3D image patches, measuring intensity levels, masking patches above or below threshold levels, and extracting features using parameters like Haar wavelet, temporal energy, and Discrete Cosine Transform Energy Band Ratios to make a binary decision on weather event detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rain detection methods are used with CCTV surveillance cameras, then detection can be performed using available camera resources, but detection accuracy deteriorates due to low resolution, high dynamic environments, and cluttered scenes
Solution Approach 1:
The video sequence is divided into multiple frames, and each frame is segmented into multiple regions of interest (ROIs). This segmentation allows the system to focus computational resources on specific areas where weather events are more likely to occur, improving detection accuracy in low-resolution CCTV footage while maintaining adaptability to different scene dynamics.
Solution Approach 2:
Different detection strategies and parameters are applied to different regions of the video sequence. The system adapts its detection sensitivity and processing methods based on local scene characteristics, such as distinguishing between sky regions and ground regions, which improves overall detection accuracy while accommodating varying camera poses and environmental conditions.
2Measurement precision
If high frame rate video sequences are assumed for weather event detection, then detection precision may improve, but device complexity and processing requirements increase
Solution Approach 1:
The system applies detection algorithms selectively to only certain regions and frames where weather events are most likely to be detected, rather than processing the entire video sequence at full resolution and frame rate. This partial processing approach maintains detection precision in critical areas while significantly reducing overall computational complexity and processing requirements.
Data Source
AI summary
A method of detecting a dynamic weather event includes the steps of: (a) receiving video images of a scene from a camera; (b) dividing each of the video images into multiple regions, in which a region is defined by a range of distances from the camera to objects in the scene; (c) selecting a region; and (d) segmenting the selected region into a plurality of three-dimensional (3D) image patches, in which each 3D image patch includes a time-sequence of T patches, with each patch comprised of N×M pixels, wherein N, M and T are integer numbers. The method also includes the following steps: measuring an image intensity level in each of the 3D image patches; masking 3D image patches containing image intensity levels that are above a first threshold level, or below a second threshold level; and extracting features in each 3D image patch that is not discarded by the masking step. Based on the extracted features, the method makes a binary decision on detecting a dynamic weather event.