Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction
Authors: Inhwan Bae, Hae-Gon Jeon911-919
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through the effective incorporation of the three parts within an end-to-end framework, DMRGCN achieves state-of-the-art performances on a variety of challenging trajectory prediction benchmarks. . . . 4 Experiments In this section, we present our experimental results for two public datasets: ETH (Pellegrini et al. 2009) and UCY (Lerner, Chrysanthou, and Lischinski 2007) which contain pedestrian trajectories and various real-world human interactions. . . . Table 1: Comparison of our DMRGCN with other state-of-the-art methods (ADE/FDE). . . . 4.2 Ablation Studies An extensive ablation study was conducted to examine the effects of different components on DMRGCN performance. |
| Researcher Affiliation | Academia | Inhwan Bae and Hae-Gon Jeon Gwangju Institute of Science and Technology (GIST) inhwanbae@gm.gist.ac.kr and haegonj@gist.ac.kr |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | In this section, we present our experimental results for two public datasets: ETH (Pellegrini et al. 2009) and UCY (Lerner, Chrysanthou, and Lischinski 2007) which contain pedestrian trajectories and various real-world human interactions. Both datasets include 5 subsets (ETH, HOTEL, UNIV, ZARA1 and ZARA2). |
| Dataset Splits | Yes | Following previous works (Alahi et al. 2016; Gupta et al. 2018; Kosaraju et al. 2019; Huang et al. 2019; Mohamed et al. 2020; Sun, Zhao, and He 2020), we adopt a leaveone-out evaluation strategy, in which four datasets are used for training and the remaining one is used for testing. |
| Hardware Specification | Yes | The training is performed on a NVIDIA 2080Ti GPU, which usually takes 12 hours. |
| Software Dependencies | No | The paper mentions the use of TCNs and PReLU as activation functions but does not specify version numbers for any software libraries or dependencies (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | We use one GCN and four TPCNN blocks, which shows the best results in ablation studies. Our model is trained for 256 epochs with the SGD optimizer. We use a mini-batch size of 128 with an initial learning rate 1e 4 and decay of rate 0.8 every 32 epochs. Data augmentation schemes such as random rotation, flip and scaling are utilized. |