SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Authors: Yinda Xu, Zeyu Wang, Zuoxin Li, Ye Yuan, Gang Yu12549-12556
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive analysis and ablation studies demonstrate the effectiveness of our proposed guidelines. Without bells and whistles, our Siam FC++ tracker achieves state-of-the-art performance on five challenging benchmarks(OTB2015, VOT2018, La SOT, GOT-10k, Tracking Net), which proves both the tracking and generalization ability of the tracker. |
| Researcher Affiliation | Collaboration | Yinda Xu,1 Zeyu Wang,2 Zuoxin Li,2 Ye Yuan,2 Gang Yu2 1College of Electrical Engineering, Zhejiang University 2Megvii Inc. |
| Pseudocode | No | The paper describes the architecture and processes of Siam FC++ but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We will release the code to facilitate further researches. |
| Open Datasets | Yes | We adopt ILSVRC-VID/DET (Russakovsky et al. 2015), COCO (Lin et al. 2014) , Youtube BB (Real et al. 2017), La SOT (Fan et al. 2019) and GOT-10k (Huang, Zhao, and Huang 2018) as our basic training set. |
| Dataset Splits | Yes | On GOT-10k val subset, we obtain an AO of 77.8 for the tracker predicting PSS and an AO of 78.0 for the tracker predicting Io U. |
| Hardware Specification | Yes | The proposed tracker with Alex Net backbone runs at 160 FPS on the VOT2018 short-term benchmark, while the one with Google Net backbone runs at about 90 FPS on the VOT2018 short-term benchmark, both evaluated on an NVIDIA RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions software like PyTorch (implied by typical deep learning setups) and ImageNet pretraining, but it does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We first train our model with for 5 warm up epochs with learning rate linearly increased from 10 7 to 2 10 3, then use a cosine annealing learning rate schedule for the rest of 45 epochs, with 600k image pairs for each epoch. We choose stochastic gradient descent (SGD) with a momentum of 0.9 as the optimizer. |