Forecast the Plausible Paths in Crowd Scenes
Authors: Hang Su, Jun Zhu, Yinpeng Dong, Bo Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets demonstrate that our method obtains the state-of-the-art performance in both structured and unstructured scenes by exploring the complex and uncertain motion patterns, even if the occlusion is serious or the observed trajectories are noisy. |
| Researcher Affiliation | Academia | Hang Su, Jun Zhu, Yinpeng Dong, Bo Zhang Tsinghua National Lab for Information Science and Technology State Key Lab of Intelligent Technology and Systems Center for Bio-Inspired Computing Research Department of Computer Science and Technology, Tsinghua University, Beijing, China {suhangss,dcszj,dongyp13,dcszb}@tsinghua.edu.cn |
| Pseudocode | No | The paper describes the proposed method using figures and mathematical equations but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement regarding the availability of source code or a link to a code repository. |
| Open Datasets | Yes | Experiments are conducted on two public datasets: the CUHK Crowd Dataset [Shao et al., 2014] that includes hundreds of crowd videos with different densities and perspective scales in many environments with each containing thousands of key point trajectories; and the subway station dataset [Zhou et al., 2011], which is a 30-minute sequence collected in the New York Grand Central Station, resulting in more than 40,000 keypoint trajectories in total. |
| Dataset Splits | Yes | In our experiments, we randomly select a half of the trajectories to train the model, and keep the rest for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper describes algorithms and methods used (e.g., LSTM, DGP, BPTT, SGD) but does not provide specific software names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | In our experiments, we use a social-aware LSTM with 128 hidden units, i.e., the input trajectories are mapped to a 128-dimensional hidden feature vector (ht); moreover, we set the latent variational variable in deep Gaussian processes as 8-dimensional vectors (zt). Moreover, we use one LSTM layer, and a two-layer Gaussian process model in the social-aware LSTM and deep Gaussian processes modules, respectively. |