POST: POlicy-Based Switch Tracking
Authors: Ning Wang, Wengang Zhou, Guojun Qi, Houqiang Li12184-12191
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive ablation studies and experimental comparisons against state-of-the-art trackers on 5 prevalent benchmarks verify the effectiveness of the proposed method. In this section, we first introduce the experimental details. Then, we analyze the effectiveness of our policy-based ensemble framework. Finally, we compare our method with state-of-the-art trackers on 5 standard tracking benchmarks including OTB-2013 (Wu, Lim, and Yang 2013), OTB-2015 (Wu, Lim, and Yang 2015), Temple-Color (Liang, Blasch, and Ling 2015), UAV123 (Mueller, Smith, and Ghanem 2016), and La SOT (Fan et al. 2019). |
| Researcher Affiliation | Collaboration | Ning Wang,1 Wengang Zhou,1 Guojun Qi,2 Houqiang Li1 1CAS Key Laboratory of GIPAS, University of Science and Technology of China 2Futurewei Technologies wn6149@mail.ustc.edu.cn, guojun.qi@huawei.com, {zhwg, lihq}@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Training of Deep Q-Network |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | 5 standard tracking benchmarks including OTB-2013 (Wu, Lim, and Yang 2013), OTB-2015 (Wu, Lim, and Yang 2015), Temple-Color (Liang, Blasch, and Ling 2015), UAV123 (Mueller, Smith, and Ghanem 2016), and La SOT (Fan et al. 2019). |
| Dataset Splits | No | The paper mentions using videos from VOT-2013, VOT-2014, and VOT-2015 for training the agent and La SOT's provided training videos. However, it does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | Yes | The experimental environment is Py Torch on a computer with 4.00GHz Intel Core I7-4790K and NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the experimental environment but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We randomly choose continuous 20 to 40 frames in a video for each episode, and our agent is trained for 2 105 episodes using Adam optimizer. The size of the replay buffer M is set to 10000. The learning rate is 0.0001, discount factor γ = 0.9 and batch size is 128. As for the ϵgreedy, we initially set ϵ = 1 and reduce it by 5% every 2000 episodes and fix it after it is reduced to 0.1. |