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T2FPV

Predicting pedestrian motion is essential for developing socially-aware robots that interact in a crowded environment. While the natural visual perspective for a social interaction setting is an egocentric view, the majority of existing work in trajectory prediction therein has been investigated purely in the top-down trajectory space. To support first-person view trajectory prediction research, we present T2FPV, a method for constructing high-fidelity first-person view (FPV) datasets given a real-world, top-down trajectory dataset; we showcase our approach on the ETH/UCY pedestrian dataset to generate the egocentric visual data of all interacting pedestrians, creating the T2FPV-ETH dataset. In this setting, FPV-specific errors arise due to imperfect detection and tracking, occlusions, and field-of-view (FOV) limitations of the camera. To address these errors, we propose CoFE, a module that further refines the imputation of missing data in an end-to-end manner with trajectory forecasting algorithms. Our method reduces the impact of such FPV errors on downstream prediction performance, decreasing displacement error by more than 10% on average. To facilitate research engagement, we release our T2FPV-ETH dataset and software tools.

CoFE Example

Dependency Installation

  • Create a conda environment:
    • conda create --name fpv python=3.7
    • conda install -c pytorch torchvision cudatoolkit=11.0 -c pytorch
    • cat requirements.txt | xargs -n 1 python -m pip install
  • In numpy/lib/format.py, ensure that pickle.dump has protocol=4

Dataset Preparation

You can either download the raw data directly and follow the pre-processing steps below, or skip directly to downloading the already processed data. We highly recommend using the pre-processed data, as that is the process which generates the scenario IDs in the correct order for an eval.ai submission.

Pre-Processed Data

  • Download input_data.pkl from here: https://cmu.box.com/s/d5t0yyirtjjkodgreiv6r48yby4mtywv
    • Move the file into the data/ folder, so that it becomes data/input_data.pkl
  • Download the processed cache files here: https://cmu.box.com/s/gzaop5av0zsteotmfzyiwu6hgckbukh8
  • Extract the npy files into the data/processed folder:
    • tar -xvf processed.tar.gz; mv processed/* data/processed/.; rm processed.tar.gz
    • This should result in the files appearing as, e.g., data/processed/eth_*_pred-dets.npy
  • Note that these files contain only the ResNet extracted features from each ego frame. If you would like to re-process them, or save the actual images themselves, please see below for handling the raw data.

Raw Data

  • Download the raw data from the following link: https://cmu.box.com/s/tij0yyo8ulqh1n7uane0pf3onj7ror7f
  • Extract the files into the data/ folder:
    • tar -xvf FPVDataset.tar.gz; mv FPVDataset data/. ; rm FPVDataset.tar.gz
  • Run python multi_run.py --run-type data to construct the processed cache files for all the tracklets, data loaders, etc.
    • You can modify the vrnntools/utils/datasets/fpv_dataset.py script as desired to e.g. extract different version of ResNet features. Make sure to modify the fpv_dataset_name function to ensure the caching is overwritten appropriately.

Running Experiments

  • To test the install, run:
    • python multi_run.py --exp-config config/fpv_noisy/vrnn.json --run-type data
  • You can then create your own trainer by
    • following the examples in config/fpv_det_train/
    • adding or modifying a trainer such as in vrnntools/trajpred_trainers/sgnet_cvae.py
    • updating run.py::run_task(...) to create the correct trainer object
  • Alternatively, you could use the generated .npy files in data/processed to extract the data. Each npy file is a list of 3 Torch DataLoader objects, corresponding to the train, val, and test data loader. You can access the underlying data by accessing the dataset field of each of these objects, to retrieve instances of FPVDataset. Examine the __getitem__ function therein to see what is returned.
    • Make sure to use the files data/processed/*_fpv_hl-8_fl-12_hs-10_fs-10_sk-1_ag-2_bal_mdl-3_imgs_dets_align_resnet_hmatch_pred-dets.npy

Submission to Eval AI

  • See https://eval.ai/web/challenges/challenge-page/2086/evaluation for more info on terms and conditions
  • For participation in the 2023 IROS workshop challenge, first train the 5 models as required:
    • python multi_run.py --exp-config config/fpv_det_train/[YOUR CONFIG].json --run-type trainval
    • This will output the predicted tensors in out/*/det_train_*/trajs/; note that these outputs will automatically filter the six trajectories down to one based on minADE to ground truth.
  • Then, run the following to generate the file to test against
    • python submission_eval_ai.py --exp-config config/fpv_det_train/[YOUR FILE].json --output submission_test.npy
    • This will process the outputted tensors from above into the correct format for eval.ai submission; examine the python script for more information