Task-Specific Detector Adaptation for Edge MOT: Tracking and Deployment Trade-Offs on the NVIDIA Jetson Nano

Although several MOT solutions have been proposed, limited evidence is available regarding how task-specific detector adaptation affects tracking quality and deployment requirements on low-cost edge hardware. These effects have not been jointly evaluated under fixed tracking and deployment conditions. This work evaluates MOT on a 4 GB NVIDIA Jetson Nano using the MOT17 benchmark. Experiment 1 served as a baseline characterization and detector-selection stage. YOLOv8n, SSDLite320 and Faster R-CNN were evaluated with a fixed OC-SORT configuration under the same tracking framework. Based on the observed trade-offs, YOLOv8 was selected for adaptation. A task-specific fine-tuning stage was subsequently performed for YOLOv8n and YOLOv8s using more than 30,000 annotated pedestrian image records. Experiment 2 constituted the main analysis. Baseline and adapted models were evaluated under the same tracking and deployment protocol. Tracking performance was assessed through MOTA, IDF1, and HOTA. Processing throughput was measured together with system RAM usage. Board power consumption and energy per frame were also measured. In the paired YOLOv8n comparison, MOTA increased from 15.95 to 51.09 after adaptation. Throughput changed from 3.6 to 3.5 FPS. System RAM usage changed from 3.3820 to 3.3925 GB. Average board power changed from 4235 to 4227 mW. Energy per frame increased by approximately 2.7%. The adapted YOLOv8s model achieved a MOTA of 55.88 at 2.5 FPS. None of the evaluated pipelines achieved conventional real-time video throughput. These findings indicate that task-specific adaptation improved tracking performance with limited changes in the measured deployment characteristics within the evaluated configuration.