AI Camera Power Control for Motion-Aware Security Recording
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Solution Overview
Problem
Security cameras often operate continuously with limited movement activity, leading to unnecessary power consumption due to infrequent image capture, processing, and storage demands, especially in battery-operated systems.
Innovation Solution
Implementing contextually-aware power reduction techniques that utilize AI-driven object detection and categorization to adjust camera settings based on detected motion and object importance, reducing frame rates, image quality, and data processing to conserve power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the camera operates continuously at full power to ensure continuous monitoring capability, then the reliability of security coverage is improved, but the power consumption increases significantly
Solution Approach 1:
The camera system dynamically adjusts its operational state based on detected motion activity. When motion is detected, the camera transitions to high-frame-rate recording mode to capture security-critical events. When no motion is detected for a threshold period, the camera transitions to low-power standby mode, reducing frame rate significantly. This dynamic state adjustment resolves the contradiction by making the system reliable when needed while conserving power during normal periods.
Solution Approach 2:
The system implements periodic motion detection checks at reduced intervals during standby mode, rather than continuous full-power operation. The camera periodically wakes up to check for motion activity, and only activates full recording when motion is detected. This periodic monitoring approach maintains security coverage reliability while dramatically reducing average power consumption compared to continuous operation.
2Use of energy by moving object
If the camera reduces frame rate during low activity periods to save power, then power consumption is reduced, but the responsiveness to detect motion decreases
Solution Approach 1:
The system introduces an intermediary low-power motion detection mode that operates between full recording and complete standby. In this intermediate state, the camera uses reduced frame rates with motion detection algorithms to monitor for activity. This intermediary state allows the system to detect motion relatively quickly while consuming significantly less power than full-rate recording, resolving the contradiction between power savings and detection speed.
Solution Approach 2:
The system performs preliminary motion detection at low power levels before transitioning to full recording mode. When motion is detected during standby or low-power mode, the system has already identified the event and can immediately transition to high-frame-rate recording to capture the full event. This preliminary detection approach ensures quick response to motion while maintaining low power consumption during normal periods.
3Manufacturing precision
If the camera processes and stores all captured image data continuously, then complete recording quality is maintained, but the computational power and storage requirements increase
Solution Approach 1:
The system extracts and processes only the essential information needed for security monitoring rather than processing all captured image data. Motion detection algorithms identify regions of interest, and the system focuses computational resources on analyzing and storing data from these critical regions. This extraction approach maintains recording quality for important events while reducing overall computational power requirements by ignoring redundant data during low-activity periods.
Solution Approach 2:
The system applies different quality levels to different regions or time periods of recording. During high-activity periods or when events are detected, the system maintains high recording quality with full processing and storage. During low-activity periods, the system reduces processing and storage requirements while maintaining adequate quality for security purposes. This local quality approach balances recording quality with computational power requirements.
Data Source
AI summary
Techniques for power reduction of a camera system are disclosed. A camera system receives, via an image sensor, a video signal including a plurality of image frames, each frame including a plurality of pixels representing a plurality of objects. The camera system detects an object in an image frame and determines an amount of motion of the object by identifying and comparing the position of the object between each frame of the plurality of frames. The camera system identifies the object as belonging to a category of objects and generates a bounding box around the object. Based on the category and amount of motion, the camera system outputs a control signal to the image sensor to set a power level of the image sensor. The camera system encodes data representing the object in the bounding box at a compression level based on quality parameters associated with the category.