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Motion-Pattern-Based Multi-Object Tracking for Real-Time Edge Computing Deployment
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AI
DOI：10.32890/jict2026.25.2.5.png)
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Abstract
En 中文
Real-time object tracking in embedded systems requires algorithms that balance processing performance with energy efficiency for practical deployment. This study evaluates the performance of the TG-Lite algorithm on Jetson Nano embedded hardware for surveillance applications requiring a minimum processing rate of 10 frames per second (FPS). The methodology involves comprehensive performance analysis, measuring frame-processing rates, energy consumption patterns, and system resource utilisation across video-file and live-stream scenarios. TG-Lite achieves 15.51 FPS processing capability with 11.24 watts (W) power consumption, meeting real-time processing requirements for embedded surveillance deployment. The algorithm demonstrates consistent performance across different input conditions, maintaining stable processing rates on both video files (10.86 FPS) and live streams (15.51 FPS). TG-Lite exhibits efficient resource utilisation, with moderate central processing unit (CPU) usage (17.4% average) and effective graphics processing unit (GPU) acceleration. The energy analysis reveals TG-Lite's 1.38 frames-per-watt efficiency, indicating effective allocation of computational resources for real-time processing demands. Performance evaluation shows that TG-Lite maintains stable tracking continuity, with an average track length of 35.8 frames and a consistent confidence distribution pattern. These results demonstrate the suitability of the proposed approach for embedded computer vision applications under the evaluated edge constraints.
Keywords:
Computer vision
embedded systems
energy efficiency
object tracking
real-time processing
Journal
J
IF:
1.3
Papers:
10
Citations:
0
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