DOI: 10.6084/m9.figshare.11455545
title: NTU Pedestrian Dataset

description: **************** NTU Pedestrian Dataset *******************<br>Attached files contain our data collected inside Nanyang Technological University Campus for pedestrian intention prediction. The dataset is particularly designed to capture spontaneous vehicle influences on pedestrian crossing/not-crossing intention.<br>We utilize this dataset in our journal paper "Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields" accepted by IEEE Transactions on Intelligent Transportation Systems.<br>The dataset consists of 35 crossing and 35 stopping* (not-crossing) scenarios. The image sequences are in 'Image_sequences' folder.<br>'stopping_instants.csv' and 'crossing_instants.csv' files provide the stopping and crossing instants respectively, utilized for labeling the data and providing ground-truth for evaluation. Camera1 and Camera2 images are synchronized. Two cameras were used to capture the whole scene of interest.<br>We provide pedestrian and vehicle bounding boxes obtained from [1]. The occlusions and mis-detections are linearly interpolated. All necessary detections are stored in 'Object_detector_pedestrians_vehicles' folder. Each column within the csv files ('car_bndbox_..') corresponds to a unique tracked car within each image sequence. Each of the pedestrian csv files ('ped_bndbox_..') contains only one column, as we consider each pedestrian in the scene separately.<br>Additional details:* [xmin xmax ymin ymax] = [left right top down] (for the bounding boxes)* Dataset frequency: 15 fps.* Camera parameters (in pixels): f = 1135, principal point = (960, 540).<br><br>Additionally, we provide semantic segmentation output [2] and our depth parameters. As the data were collected in two phases, there are two files in each folder, highlighting the sequences in each phase.<br>Crossing sequences 1-28 and stopping sequences 1-24 were collected in Phase 1, while crossing sequences 29-35 and stopping sequences 25-35 were collected in Phase 2.<br>We obtained the optical flow from [3]. Our model (FLDCRF) codes are available here: https://github.com/satyajitneogiju/FLDCRF-for-sequence-labeling<br><br>If you use our dataset in your research, please cite our paper(s):1. S. Neogi, M. Hoy, K. Dang, H. Yu, J. Dauwels, "Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields". Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2019.<br><br>2. "S. Neogi, M. Hoy, W. Chaoqun, J. Dauwels, 'Context Based Pedestrian Intention Prediction Using Factored Latent Dynamic Conditional Random Fields', IEEE SSCI-2017."<br><br>Please email us if you have any questions:<br>1. Satyajit Neogi, PhD Student, Nanyang Technological University @ satyajit001@e.ntu.edu.sg2. Justin Dauwels, Associate Professor, Nanyang Technological University @ jdauwels@ntu.edu.sg<br><br>Our other group members include:<br>3. Dr. Michael Hoy, @ mch.hoy@gmail.com4. Dr. Kang Dang, @ kangdang@gmail.com5. Ms. Lakshmi Prasanna Kachireddy,6. Mr. Mok Bo Chuan Lance, and7. Mr. Xu Yan <br><br>References:<br>1. S. Ren, K. He, R. Girshick, J. Sun, ``Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS 2015.2. A. Kendall, V. Badrinarayanan, R. Cipolla, ``Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding", BMVC 2017.3. C. Liu. ``Beyond Pixels: Exploring New Representations and Applications for Motion Analysis". Doctoral Thesis. Massachusetts Institute of Technology. May 2009.<br><br><br>* Please note, we had to remove sequence Stopping-33 for privacy reasons.<br>FUNDINGSTE-NTU NRF corporate lab@university scheme