Depth Camera Distance Measurement for Home Security
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Solution Overview
Problem
Conventional 2D cameras in home security applications cannot accurately determine the distance of an object and often misjudge whether an object is approaching, due to image distortions from shadows and the environment, leading to incorrect recognition.
Innovation Solution
A monitoring device equipped with a depth camera, processor, and memory that captures depth image data, estimates object distance, and performs human body and face recognition, controlling recording operations based on predefined distance thresholds to reduce misjudgments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 2D camera is used for environmental monitoring, then the device complexity is low, but the measurement precision of object distance is insufficient
Solution Approach 1:
The patent transitions from 2D image capture to 3D depth image capture by introducing a depth camera. This dimensional change enables the system to obtain depth information and accurately measure object distances, directly resolving the measurement precision limitation of 2D cameras while maintaining reasonable device complexity through integrated depth sensing technology.
2Reliability
If a 2D camera is used for object recognition, then the device complexity is low, but the reliability of recognition is insufficient due to shadow and environment interference
Solution Approach 1:
By capturing depth information in the third dimension, the system can distinguish objects from shadows and environmental artifacts. The depth data provides additional spatial context that enables more reliable object recognition and identification, overcoming the limitations of 2D imaging where shadows and lighting conditions cause misjudgments.
3Reliability
If the depth camera continuously records video data, then the reliability of monitoring is improved, but the loss of energy increases
Solution Approach 1:
The system implements periodic monitoring with conditional video recording. The depth camera continuously captures depth images at lower energy consumption, and video recording is activated periodically or triggered only when specific conditions are met (such as detecting human presence or objects within threshold distance). This periodic action maintains monitoring reliability while significantly reducing overall energy loss compared to continuous video recording.
Solution Approach 2:
The system dynamically changes operational parameters based on detected conditions. When no objects of interest are detected, the camera operates in a low-power depth-only mode. When objects are detected within the threshold distance, the system switches to high-resolution video recording mode. This parameter change strategy ensures reliable monitoring coverage while optimizing energy consumption by matching recording intensity to actual monitoring needs.
Data Source
AI summary
A monitoring method includes the following operations: capturing depth image data of a predetermined environment and generating estimated distance of an object in the predetermined environment; comparing the depth image data with human body feature; and controlling the depth camera to perform a recording operation when the depth image data conforms to the human body feature and when the estimated distance is less than a first distance threshold, in order to generate video data.