Deep Attentive Tracking via Reciprocative Learning
Authors: Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on large-scale benchmark datasets show that the proposed attentive tracking method performs favorably against the state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1Beijing University of Posts and Telecommunications, Beijing, China {pushi_519200, zhhg}@bupt.edu.cn 2Tencent AI Lab, Shenzhen, China dynamicstevenson@gmail.com 3Shanghai Jiao Tong University, Shanghai, China chaoma@sjtu.edu.cn 4University of California at Merced, Merced, U.S.A mhyang@ucmerced.edu |
| Pseudocode | No | The paper describes the proposed method and tracking process in detail but does not include any formal pseudocode blocks or algorithms. |
| Open Source Code | No | We present more experimental results in the supplementary materials, and will make the source code available to the public. |
| Open Datasets | Yes | Finally, we evaluate our method on the standard benchmarks, i.e., OTB-2013 [50], OTB-2015 [51] and VOT-2016 [28]. |
| Dataset Splits | No | The paper describes online sampling and updates for training an online tracker but does not specify explicit training/validation/test dataset splits with percentages or counts for the benchmark datasets used for evaluation. |
| Hardware Specification | Yes | Our implementation is based on pytorch [37] and runs on a PC with an i7-3.4 GHz CPU and a Ge Force GTX 1080 GPU. |
| Software Dependencies | No | Our implementation is based on pytorch [37] and runs on a PC with an i7-3.4 GHz CPU and a Ge Force GTX 1080 GPU. The paper mentions 'pytorch' but does not specify a version number. |
| Experiment Setup | Yes | In the first frame, the number N1 of samples is set to 5500. We train the randomly initialized classifier using H1 = 50 iterations with a learning rate of 2e-4. In each iteration, we feed 1 mini-batch containing 32 positive and 32 negative samples into the network. In the online model update step, we fine-tune the classifier using H2 = 15 iterations in every T = 10 frames with a learning rate of 3e-4. The network solver is stochastic gradient descent (SGD). During online detection, the number N2 of proposals is set to 256. We set λ between 0 to 8 at an interval of 1 to evaluate the tracking performance on the OTB-2013 dataset. In the following experiments, we fix λ = 5 to report our tracking results. |