Depth Mapping for Real-Time Object Recognition on Edge Devices
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Solution Overview
Problem
Existing object recognition systems are prone to false positives and negatives, especially in real-time applications on limited hardware such as UAVs and edge devices, due to computational constraints and the need for complex neural networks, which are resource-intensive and require significant processing power.
Innovation Solution
The use of depth mapping as a pre-processing step to isolate objects before recognition, reducing the computational load on the object recognizer by focusing on regions closer to the camera and lowering detection thresholds, thereby improving accuracy and efficiency on less powerful hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex neural networks are used for real-time object recognition, then recognition accuracy is improved, but computational resources and power consumption increase significantly
Solution Approach 1:
The patent applies depth map generation as a preliminary action before object recognition. By pre-processing the video stream to create depth maps that highlight foreground objects and suppress background, the system prepares optimized input data for the recognition model. This preliminary depth-based filtering reduces the complexity of the recognition task, allowing accurate detection with less computationally intensive models, thereby reducing power consumption while maintaining accuracy.
2Measurement precision
If complex neural networks are used for real-time object recognition, then recognition accuracy is improved, but processing speed decreases
Solution Approach 1:
Depth map generation serves as a preliminary action that simplifies the input data before recognition. The depth map pre-processing step creates a simplified representation where foreground objects are enhanced and background is suppressed, reducing the computational burden on the recognition model and enabling faster processing while maintaining accuracy.
Solution Approach 2:
The patent segments the processing into distinct stages: depth map generation followed by object recognition. This segmentation allows the system to handle different aspects of the recognition task separately, with the depth map stage preparing simplified input that accelerates the subsequent recognition phase, improving overall processing speed.
3Reliability
If stereo camera input is used to generate depth maps, then false positives are reduced, but device complexity and cost increase
Solution Approach 1:
The patent makes the depth map generation process universal by implementing it through software algorithms that can work with standard single-camera systems. The depth estimation is achieved through computational methods rather than requiring specialized stereo camera hardware, allowing the same approach to be applied across different device types without increasing hardware complexity.
Solution Approach 2:
The patent replaces the mechanical/stereo optical system with a computational approach. Instead of relying on physical stereo camera setups to generate depth information, the system uses algorithms to estimate depth from monocular video input, substituting mechanical complexity with software-based solutions that achieve the same reliability benefits.
4Measurement precision
If depth map generation is performed for every frame, then object recognition accuracy is improved, but computational load increases
Solution Approach 1:
The patent implements periodic action by generating depth maps at specific intervals or selectively for certain frames rather than continuously for every frame. This approach maintains the accuracy benefits of depth-based recognition while reducing the overall computational load by processing only when necessary, such as when object detection is needed or at fixed time intervals.
Data Source
AI summary
A method for performing real-time recognition of objects in motion includes receiving an input video stream from a camera, generating one or more depth maps for one or more frames of the input video stream, recognizing one or more objects in a current frame based on corresponding depth map using a machine learning algorithm, and displaying the one or more recognized objects in the current frame in one or more bounding boxes.