Motion-Triggered Object Detection for Edge Devices
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Solution Overview
Problem
Traditional real-time object detection systems are computationally expensive and require powerful hardware, leading to delayed outputs on less powerful devices like small computers without GPUs, and cloud processing is slow, making real-time analysis impractical for edge devices that lack processing power.
Innovation Solution
A method and system for real-time object detection that uses motion-triggered detection and interpolation between tracking and object detection algorithms, reducing computational load by only processing frames with detected motion, allowing for seamless switching between object recognition and tracking, and executing these processes in separate threads to maintain smooth performance on CPU-limited devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional real-time object detection systems process every frame from video camera, then object detection accuracy is maintained, but computational cost increases and hardware requirements become more demanding
Solution Approach 1:
The system segments the video processing task into two distinct modules: a motion detection module that processes every frame to detect changes, and an object detection module that only processes frames where motion is detected. This segmentation allows the computationally expensive object detection to be performed selectively rather than continuously, reducing overall computational cost while maintaining detection accuracy when objects are present
Solution Approach 2:
The system performs partial action by applying object detection algorithms only to a subset of frames (those containing motion) rather than all frames. This partial processing approach maintains sufficient detection accuracy for security applications while dramatically reducing the computational burden compared to processing every frame
2Reliability
If object detection is performed on every frame, then detection reliability is improved, but output latency increases on less powerful hardware
Solution Approach 1:
The processing pipeline is segmented into motion detection (performed on every frame) and object detection (performed only on motion-containing frames). This ensures that the system reliably detects all motion events while reducing the time penalty associated with running full object detection on every frame, thereby improving output latency on powerful hardware
3Device complexity
If cloud processing is used for object detection, then processing power requirements are reduced, but real-time analysis capability is lost
Solution Approach 1:
The system performs partial object detection only on frames containing motion, which reduces the total computational workload to a level that can be handled by edge devices with limited processing power. This selective processing enables real-time analysis capability to be maintained on local hardware without requiring cloud connectivity
4Power
If motion-triggered detection is used, then computational load is reduced, but detection coverage may be limited to only moving objects
Solution Approach 1:
The system achieves multi-functionality by combining two detection approaches: motion-triggered detection for detecting moving objects (reducing computational load) and periodic full-frame object detection for detecting stationary objects (maintaining detection coverage). This universal approach allows the system to handle both moving and stationary targets effectively
Data Source
AI summary
A method for performing real-time detection of objects in motion includes receiving an input video stream from a camera, detecting if a motion has occurred in a current frame of the input video stream, providing the current frame for object detection if the motion has been detected therein, detecting a moving object in the current frame, displaying the detected moving object, simultaneously tracking a location of the detected moving object within corresponding frame, while the object detection continues for one or more moving objects, and generating a tracking box and overlaying the tracking box on the detected moving object and then transmitting the video to the display, and continuing the tracking of the detected moving object till the object detection continues for corresponding one or more moving objects.