Video Object Tracking On Nv Vot211
Métriques
AUC
Precision
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AUC | Precision |
|---|---|---|
| towards-grand-unification-of-object-tracking | 34.52 | 47.77 |
| mixformer-end-to-end-tracking-with-iterative-1 | 39.23 | 54.20 |
| target-aware-tracking-with-long-term-context | 39.29 | 53.94 |
| towards-sequence-level-training-for-visual | 37.22 | 51.70 |
| a-distilled-model-for-tracking-and-tracker | 23.58 | 30.64 |
| high-speed-tracking-with-multi-kernel | 28.04 | 34.94 |
| transformer-meets-tracker-exploiting-temporal | 36.66 | 50.68 |
| procontext-exploring-progressive-context | 40.10 | 54.50 |
| fast-online-object-tracking-and-segmentation | 35.14 | 46.49 |
| in-defense-of-color-based-model-free-tracking | 17.52 | 21.60 |
| transforming-model-prediction-for-tracking | 39.25 | 53.01 |
| 190407220 | 35.89 | 48.68 |
| know-your-surroundings-exploiting-scene | 36.02 | 48.13 |
| neighbortrack-improving-single-object | 38.32 | 52.54 |
| 2103-15436 | 36.79 | 51.97 |
| adtrack-target-aware-dual-filter-learning-for | 31.82 | 40.31 |
| staple-complementary-learners-for-real-time | 31.29 | 39.12 |
| autoregressive-visual-tracking | 35.92 | 51.64 |
| robust-visual-tracking-by-segmentation | 36.20 | 53.68 |
| high-performance-visual-tracking-with-siamese | 33.92 | 44.04 |
| learning-target-candidate-association-to-keep | 39.59 | 55.50 |
| lighttrack-finding-lightweight-neural | 32.85 | 43.65 |
| learning-spatio-temporal-transformer-for | 38.26 | 51.37 |
| distractor-aware-siamese-networks-for-visual | 31.12 | 39.09 |
| deeper-and-wider-siamese-networks-for-real | 35.18 | 46.18 |
| exploring-enhanced-contextual-information-for-1 | 41.50 | 54.20 |
| context-aware-correlation-filter-tracking | 30.68 | 38.57 |
| aiatrack-attention-in-attention-for | 38.91 | 53.47 |
| learning-spatially-regularized-correlation | 31.59 | 40.31 |
| learning-spatial-temporal-regularized | 27.86 | 36.18 |
| fast-visual-object-tracking-with-rotated | 35.22 | 46.57 |
| Modèle 32 | 19.55 | 25.29 |
| robust-estimation-of-similarity | 27.72 | 35.19 |
| learning-background-aware-correlation-filters | 26.29 | 35.05 |
| stmtrack-template-free-visual-tracking-with | 36.84 | 50.34 |
| in-defense-of-color-based-model-free-tracking | 23.09 | 29.14 |
| odtrack-online-dense-temporal-token-learning | 39.60 | 55.80 |
| efficient-visual-tracking-with-exemplar | 34.38 | 46.98 |
| learning-to-fuse-asymmetric-feature-maps-in | 35.80 | 48.31 |
| joint-feature-learning-and-relation-modeling | 38.59 | 53.06 |
| ocean-object-aware-anchor-free-tracking | 32.86 | 46.72 |
| high-speed-tracking-with-kernelized | 21.31 | 26.51 |
| high-speed-tracking-with-kernelized | 24.10 | 32.06 |
| fully-convolutional-siamese-networks-for | 32.62 | 40.81 |