Research on target detection and tracking algorithms in real-time video streaming
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29 set 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 29 set 2025
Ricevuto: 27 gen 2025
Accettato: 09 mag 2025
DOI: https://doi.org/10.2478/amns-2025-1130
Parole chiave
© 2025 Wenjing Yu, Daitao Wang, Hongjun Qiu and Jianbin Zhong, published by Sciendo.
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Comparison of improved inter-frame difference method on UAV123 dataset
| Detection method | Precision% | MAP% | Recall% | FPS (f/s) | |||
|---|---|---|---|---|---|---|---|
| Car | Bus | Van | Others | ||||
| Interframe difference method | 86.09 | 86.01 | 84.37 | 84.87 | 85.34 | 84.68 | 24 |
| Recluster the anchor box only | 87.27 | 88.98 | 86.36 | 85.54 | 87.04 | 86.53 | 29 |
| Only after improving the extraction network | 89.56 | 87.88 | 88.36 | 86.41 | 88.05 | 86.41 | 25 |
| Only after improving the loss function | 90.15 | 87.54 | 91.29 | 87.09 | 89.02 | 87.96 | 30 |
| Improved interframe difference method+Mean shift | 96.62 | 94.28 | 94.68 | 93.22 | 94.70 | 89.92 | 32 |
Comparison of results of different target detection algorithms
| Detection method | Precision% | MAP% | Recall% | FPS (f/s) | Parameter quantity/M | GFLOPs/G | |||
|---|---|---|---|---|---|---|---|---|---|
| Car | Bus | Van | Others | ||||||
| Faster-RCNN | 89.63 | 88.36 | 90.88 | 88.21 | 89.27 | 85.96 | 11 | 51.1 | 124.1 |
| SSD-512 | 87.84 | 86.43 | 85.64 | 87.7 | 86.90 | 84.24 | 15 | 47.6 | 129.7 |
| YOLOv3 | 84.36 | 85.06 | 85.61 | 86.07 | 85.28 | 83.32 | 8 | 48.7 | 109.7 |
| YOLOx | 85.97 | 86.81 | 86.72 | 85.10 | 86.15 | 82.56 | 21 | 38.1 | 114.28 |
| YOLOv7 | 87.18 | 88.58 | 87.93 | 89.52 | 88.30 | 86.36 | 23 | 49.8 | 117.3 |
| OURS | 96.62 | 94.28 | 94.68 | 93.22 | 94.70 | 89.92 | 32 | 37.4 | 112.9 |
MOT16 comparison experiment
| Method | Accuracy | Precision | Recall | MT | ML | MOTA | IDF1 | IDSW |
|---|---|---|---|---|---|---|---|---|
| DeepSORT2 | 95.37% | 93.72% | 94.54% | 35.21% | 18.18% | 62.53 | 61.20 | 894 |
| JDE | 94.15% | 93.89% | 94.02% | 35.72% | 17.21% | 63.39 | 67.36 | 1022 |
| RAR16 | 87.14% | 85.18% | 86.15% | 33.76% | 18.62% | 61.22 | 64.33 | 560 |
| TAP | 92.14% | 90.73% | 91.43% | 37.84% | 19.97% | 65.50 | 67.31 | 839 |
| CNNMTT | 94.59% | 93.41% | 94.00% | 37.59% | 20.74% | 68.06 | 71.49 | 1106 |
| POI | 94.48% | 94.12% | 94.30% | 38.74% | 21.42% | 63.42 | 70.86 | 699 |
| TubeTK | 95.39% | 95.37% | 95.11% | 39.20% | 17.48% | 69.47 | 71.39 | 1185 |
| CTracker | 96.18% | 96.18% | 96.37% | 40.18% | 17.84% | 71.30 | 72.05 | 1244 |
| OURS | 97.57% | 98.82% | 98.19% | 43.86% | 16.62% | 75.76 | 73.37 | 1376 |
Cloud server configuration
| Module | Model/Version |
|---|---|
| Internal memory | 4 Cores |
| Graphics card | 32GB |
| Hard disk | Tesla V108 |
| Operating system | Ubuntu |
| Python | 3.8.15 |
| CUDA | 11.8 |
| CuDnn | 8.2.0 |
MOT17 comparison experiment
| Method | Accuracy | Precision | Recall | MT | ML | MOTA | IDF1 | IDSW | FP | FN |
|---|---|---|---|---|---|---|---|---|---|---|
| DeepSORT2 | 96.76% | 94.62% | 95.43% | 38.34% | 17.45% | 64.78 | 63.56 | 1534 | 22746 | 143976 |
| JDE | 95.55% | 94.65% | 93.49% | 39.03% | 16.34% | 65.43 | 68.40 | 1735 | 22420 | 193791 |
| RAR16 | 90.56% | 87.25% | 88.43% | 36.51% | 17.35% | 62.90 | 64.78 | 1575 | 19527 | 135461 |
| TAP | 94.50% | 92.07% | 93.01% | 38.52% | 17.43% | 66.34 | 68.50 | 1903 | 18811 | 183747 |
| CNNMTT | 95.43% | 94.20% | 93.94% | 39.07% | 19.34% | 68.96 | 75.63 | 2154 | 15345 | 184952 |
| POI | 95.32% | 95.38% | 95.30% | 40.39% | 21.07% | 65.04 | 72.11 | 1865 | 22966 | 167528 |
| TubeTK | 96.03% | 96.12% | 96.09% | 43.56% | 16.40% | 71.33 | 73.75 | 2287 | 16778 | 168269 |
| CTracker | 96.77% | 96.84% | 97.14% | 45.43% | 15.45% | 73.986 | 72.94 | 2335 | 15549 | 149950 |
| OURS | 98.91% | 99.43% | 99.52% | 49.45% | 14.34% | 80.61 | 75.37 | 3408 | 10184 | 129203 |