Panoramic Camera Region Extraction via Deep Learning
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Solution Overview
Problem
Current surveillance video systems require manual effort to extract regions of interest, leading to inefficiencies in labor, time, and storage due to the need for full video storage and playback, especially in high-information environments like transportation hubs.
Innovation Solution
A panoramic camera system with multiple camera units, image processors, and machine learning capabilities that automatically identifies, extracts, and synthesizes regions of particular interest from panoramic images, reducing the need for manual processing and optimizing storage by generating compact, information-rich close-up images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual extraction of regions of interest is used, then labor and time consumption increase, but storage requirements remain high
Solution Approach 1:
The system performs self-service by automatically detecting and extracting regions of interest without human intervention. The processor autonomously analyzes surveillance video streams, identifies target objects, extracts their regions, and generates close-up images, eliminating the need for manual operation and significantly improving extraction efficiency while reducing time loss.
2Quantity of substance
If full surveillance video is stored, then storage space is consumed, but information density is low
Solution Approach 1:
The system extracts only the essential information (regions of interest containing target objects) from the full surveillance video. By identifying and extracting these specific regions, the system generates compact close-up images that concentrate important information while discarding unnecessary background data, thereby reducing storage space requirements while maintaining high information density.
3Productivity
If automated region extraction is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The processor is designed as a multi-functional device that simultaneously performs video stream analysis, target object detection, region extraction, and close-up image generation. By consolidating these multiple functions into a single universal processing unit, the system achieves high processing speed while minimizing the increase in overall system complexity.
Data Source
AI summary
A panoramic camera includes a casing, at least two independent camera units fixed on the casing, a first image processor electrically connected to each image sensor, and a device to sense level of ambient light. The camera units acquire images captured by each of the camera units and can stitch the images together to form a panoramic image. A second image processor is electrically connected to the first image processor, the second image processor obtains the panoramic image from the first image processor and by training or deep learning can focus on and zoom into a region of particular interest (ROPI) in the panoramic image. The ROPI can be edited and clipped out and the clipped images can be synthesized to form close-up images of the ROPI.