Visual Object Tracking On Otb 2015
Metriken
AUC
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AUC |
|---|---|
| a-twofold-siamese-network-for-real-time | 0.657 |
| learning-target-candidate-association-to-keep | 0.709 |
| seqtrack-sequence-to-sequence-learning-for | 0.683 |
| siamvgg-visual-tracking-using-deeper-siamese | 0.654 |
| odtrack-online-dense-temporal-token-learning | 0.723 |
| learning-historical-status-prompt-for | 0.71 |
| gradnet-gradient-guided-network-for-visual | - |
| a-distilled-model-for-tracking-and-tracker | 0.701 |
| odtrack-online-dense-temporal-token-learning | 0.724 |
| visual-tracking-via-adaptive-spatially | 0.692 |
| end-to-end-representation-learning-for | 0.568 |
| samurai-adapting-segment-anything-model-for-1 | 0.715 |
| stmtrack-template-free-visual-tracking-with | 0.719 |
| do-different-tracking-tasks-require-different | 0.618 |
| scale-equivariance-improves-siamese-tracking | 0.66 |
| aaa-adaptive-aggregation-of-arbitrary-online | 0.70 |
| improving-visual-object-tracking-through | 0.712 |