Dynamic Detection Accuracy Switching for Real-Time Object Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time object tracking in video analysis using deep learning is challenging due to the trade-off between processing speed and accuracy, where high-accuracy models require many CPUs or GPUs, making them difficult to install in facilities, and low-accuracy models lead to detection failures.
Innovation Solution
An object tracking device that uses a combination of object detection and tracking units, with a control unit dynamically switching between multiple detection programs of varying accuracy based on detection and tracking results to maintain accurate object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-accuracy deep learning model is used for object detection, then detection accuracy is improved, but processing speed becomes slow and real-time tracking becomes difficult
Solution Approach 1:
The system dynamically switches between high-accuracy and low-accuracy detection programs based on tracking state. When an object is lost (tracking failure), the system activates a high-accuracy detection program to rediscover the object. When tracking is successful, it switches to a low-accuracy program for faster processing. This dynamic adaptation resolves the contradiction by applying high accuracy only when necessary.
Solution Approach 2:
The system changes the parameter of detection accuracy by selecting different detection programs with different accuracy levels. Instead of using a fixed high-accuracy model, the system adjusts the accuracy parameter dynamically based on whether tracking is successful or failed, thereby balancing accuracy and processing speed.
2Speed
If a lightweight and low-accuracy deep learning model is used for real-time tracking, then processing speed is improved, but detection failure increases and tracking becomes difficult
Solution Approach 1:
The system uses a low-accuracy detection program during normal tracking to maintain high processing speed, but dynamically switches to a high-accuracy program when tracking failures are detected. This dynamic switching ensures that reliability is maintained at critical moments while preserving speed during stable operation.
Solution Approach 2:
The system monitors tracking results and uses this feedback to control which detection program is active. When tracking succeeds, the feedback indicates continued use of the low-accuracy program. When tracking fails, the feedback triggers switching to the high-accuracy program, thereby maintaining reliability without sacrificing overall processing speed.
3Measurement precision
If a high-accuracy deep learning model is used, then detection accuracy is improved, but computational resources (CPUs and GPUs) increase making installation difficult
Solution Approach 1:
The detection system is segmented into multiple programs with different accuracy levels and resource requirements. Instead of deploying a single high-accuracy model that requires extensive computational resources, the system divides functionality into a low-accuracy program for normal operation and a high-accuracy program for corrective action, reducing overall resource requirements.
Solution Approach 2:
The system changes the accuracy parameter of the detection program based on operational needs rather than maintaining maximum accuracy continuously. This parameter adjustment allows the system to achieve sufficient detection accuracy while significantly reducing computational resource requirements and simplifying installation.
4Measurement precision
If object tracking is performed using similarity calculation from feature amounts, then tracking accuracy is improved, but computational amount increases
Solution Approach 1:
The system applies high-accuracy detection only partially - specifically when tracking failures occur - rather than continuously. During normal tracking operation, it uses lower computational methods. This partial application of high accuracy resolves the contradiction by achieving necessary tracking accuracy without the continuous computational burden.
Data Source
AI summary
In an object tracking device, an object detection unit detects an object from video data using a plurality of object detection programs of different detection accuracy. The object tracking unit tracks the object from the detected position of the object by the object detection unit. The control unit controls the object detection programs used by the object detection unit based on the detection result of the object by the object detection unit and the tracking result of the object by the object tracking unit.