Surveillance Camera Control for Automated Teacher Image Collection
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Solution Overview
Problem
Existing surveillance systems face inefficiencies in collecting teacher images for machine learning, particularly in environments where obtaining such images manually is difficult or impractical, such as construction sites, leading to suboptimal object detection accuracy.
Innovation Solution
A control apparatus and method that switches between manual and automatic surveillance modes, using a trained model to detect objects and adjust imaging ranges, allowing for efficient collection of teacher images through manual operations like pan, tilt, and zoom changes, and incorporating these images into machine learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection of teacher images is performed in difficult environments like construction sites, then detection accuracy improves, but time consumption and operational difficulty increase
Solution Approach 1:
The surveillance camera system automatically performs pan, tilt, and zoom operations to capture teacher images of detected objects without requiring manual intervention. The system serves itself by autonomously navigating to objects, adjusting imaging ranges, and collecting training data, thereby eliminating the time-consuming manual collection process while maintaining high detection accuracy
Solution Approach 2:
The system performs preliminary actions by automatically capturing and storing teacher images during normal surveillance operations. When objects are detected, the camera proactively adjusts its position and zoom level to capture high-quality images in advance, preparing training data before it would be needed for model retraining, thus saving time when detection accuracy needs to be improved
2Measurement precision
If manual pan, tilt, and zoom operations are performed to adjust imaging range, then teacher image quality improves, but operational complexity increases
Solution Approach 1:
The surveillance camera system autonomously performs pan, tilt, and zoom operations based on object detection results. The system calculates the necessary angular adjustments and zoom levels automatically, executes the movements, and captures high-quality teacher images without requiring operators to manually adjust each parameter, thereby maintaining image quality while dramatically simplifying operations
Solution Approach 2:
The system uses feedback from object detection algorithms to automatically determine the appropriate pan, tilt, and zoom adjustments needed to capture high-quality teacher images. The detection results provide real-time information about object position and size, which feeds back to the camera control system to automatically adjust imaging parameters, eliminating the need for manual operation while ensuring image quality
Data Source
AI summary
A control apparatus includes a processor that controls a surveillance camera. The processor enables switching between a first surveillance mode in which the surveillance camera is caused to perform imaging to acquire a first captured image and an imaging range is changed according to a given instruction, and a second surveillance mode in which the surveillance camera is caused to perform imaging to acquire a second captured image, a trained model that has been trained through machine learning is used to detect an object that appears in the second captured image, and the imaging range is changed according to a detection result, and outputs the first captured image acquired in the first surveillance mode as a teacher image for the machine learning.