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M2P3

This repository contains the code for the paper "M2P3: Multimodal Multi-Pedestrian Path Prediction by Self-Driving Cars With Egocentric Vision" https://www.dfki.de/~klusch/i2s/Paper_1137_-_M2P3.pdf

Dependencies

The code was tested on Ubuntu 16.04,Python 3 and a GTX 1080ti gpu . The following dependencies are needed:

numpy
scipy
Pillow
cython
matplotlib
scikit-image
tensorflow>=1.3.0
keras>=2.0.8
opencv-python
h5py
imgaug
scikit-learn

The dependencies can be installed by using "pip install"

Instructions

Train/test a model on the JAAD dataset. The model is currently using just the past bounding boxes of the pedestrians to make a prediction. The model observes 0.5 seconds in the past and predicts 1 second into the future.

  1. To train a model run:
 python m2p3.py --train

This will train a model with the default hyperparameters and will save the model in the models/ folder.

  1. To test and visualize a model run:
 python m2p3.py --test --model path_to_model_file -vis

This will visualize the predictions in the results/ folder. You can also use the --num_samples parameter to specify how many predictions the model will output. If --num_samples > 3 the predictions will be clustered into 3 trajectories (using k-means), assigning a probability to each.