Online Decision Based Visual Tracking via Reinforcement Learning
Authors: ke Song, Wei Zhang, Ran Song, Yibin Li
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results show that our DTNet achieves stateof-the-art tracking performance as well as a good balance between accuracy and efficiency. |
| Researcher Affiliation | Academia | Ke Song Wei Zhang Ran Song Yibin Li School of Control Science and Engineering, Shandong University, Jinan, China songke_vsislab@mail.sdu.edu.cn {davidzhang,ransong,liyb}@sdu.edu.cn |
| Pseudocode | Yes | Please refer to Algorithm 1 in the supplementary material available at the website mentioned in the abstract for the details of the whole training process. |
| Open Source Code | No | The project website is available at https://vsislab.github. io/DTNet/. The paper provides a link to a 'project website' but does not explicitly state that source code for the methodology is available at this link or elsewhere. |
| Open Datasets | Yes | In this section, we conduct comparative evaluations on the benchmarks including OTB-2013 [37], OTB-50 [38], OTB-100 [38], La SOT [12], Tracking Net [24], UAV123 [23] and VOT18 [18]... The sequences from VID [28] and Youtube_BB [27] datasets are used to train the DTNet |
| Dataset Splits | No | The sequences from VID [28] and Youtube_BB [27] datasets are used to train the DTNet including the decision and the tracker modules for 6 × 10^5 episodes with Adam optimizer. The paper mentions datasets used for training and evaluation on benchmarks, but does not explicitly state a validation split percentage, sample counts, or a specific methodology for creating a validation set within its own experimental setup. |
| Hardware Specification | Yes | The experiments were implemented in Py Torch on a computer with a 3.70GHz Intel Core i7-8700K CPU and two NVIDIA GTX 1080Ti GPUs. |
| Software Dependencies | No | The experiments were implemented in Py Torch on a computer with a 3.70GHz Intel Core i7-8700K CPU and two NVIDIA GTX 1080Ti GPUs. The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | We set the capacity of the replay buffer to 5000, the learning rate to 0.0001, the discount factor γ in Equ. 1 to 0.2, the batch size to 128 and nκ is set to 3 × 10^5. For the ϵ-greedy algorithm, ϵ is set to 1 and decays to 0.1 gradually. |