PoseHD: Boosting Human Detectors Using Human Pose Information
Authors: Zhijian Liu, Bowen Pan, Yuliang Xiu, Cewu Lu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on multiple pedestrian benchmark datasets validate that our proposed Pose HD framework can generally improve the overall performance of recent state-of-the-art human detectors (by 2-4% in both m AP and MR metrics). |
| Researcher Affiliation | Academia | Shanghai Jiao Tong University, Shanghai, China 200240 |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for its methodology. |
| Open Datasets | Yes | The performance of Pose HD is evaluated across three pedestrian benchmark datasets: INRIA (Dalal and Triggs 2005), ETH (Ess, Leibe, and Van Gool 2007) and PASCAL VOC 2007 (Everingham et al. 2010). |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not provide specific training/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We use Tensor Flow (Abadi et al. 2016) and Nvidia Titan X GPU to train the classification network. |
| Software Dependencies | No | The paper mentions software like Tensor Flow, Adam, and Lambda MART, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We initialize the weights randomly from scratch, and the optimization is carried out using Adam (Kingma and Ba 2015) with β1 = 0.9 and β2 = 0.999. We use a fixed learning rate of γ = 10 3 and mini-batch size of 128. |