TSC-Net: Prediction of Pedestrian Trajectories by Trajectory-Scene-Cell Classification
Authors: BO HU, Tat-Jen Cham
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative experiments show that TSC-Net achieves the SOTA performance on several datasets with most of the metrics. Especially for the goal estimation, TSC-Net is demonstrated better on predicting goals for trajectories with irregular speed. ... We demonstrate our approach outperforms most of existing methods in two datasets. |
| Researcher Affiliation | Academia | Bo Hu, Tat-Jen Cham College of Computing and Data Science Nanyang Technological University 50 Nanyang Ave, Block N4, Singapore EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual explanations and a framework diagram (Figure 2), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code of the TSC-Net is released at github.com/hubovc/TSC-Net. |
| Open Datasets | Yes | Stanford Drone Dataset (SDD) Robicquet et al. (2016) is a large-scale benchmark which contains more than 11,000 pedestrians with 20 different scenes. ... Intersection Drone Dataset (In D) Bock et al. (2020) contains about 10,000 pedestrians in 4 different road intersection scenes. ETH-UCY dataset Lerner et al. (2007); Pellegrini et al. (2009) includes 5 subsets |
| Dataset Splits | Yes | Prediction Settings in the experiments include short-term prediction and long-term prediction. Most of previous works focus on the short-term setting with T=20 and τ=8, where the source videos are down-sampled to 2.5 fps. The short-term setting is applied in the experiments on SDD and ETH-UCY datasets. Following Mangalam et al. (2021), we apply the long-term setting T=35 and τ=5 with 1 fps frame rate, which is applied in the experiment on SDD and in D. ... The ETH-UCY dataset ... the evaluation follows the leave-one-out validation strategy over 5 subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions general software components and frameworks like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNN), Transformers, and Multi-Layer Perceptron (MLP), but does not specify any particular software libraries with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | No | The paper describes the general architecture and loss function (Equation 11) with weights λ and α but does not provide their specific values or other concrete hyperparameters like learning rate, batch size, or optimizer settings for training. |