This repository corresponds to the official source code of the CVPR 2019 paper:
To get an overview about the method and its results, we highly recommend checking our poster and a short video at [Page]
Requirements
- Tensorflow-gpu 1.14.
- opencv-python, sklearn, matplotlib, Pillow (via pip).
Setup
We use the source code from WEMD[1] to compute our SEMD evaluation metric.
- extract the blitz++.zip under /wemd.
- cd build
- cmake ..
- make
After compilation, you should get a library under /wemd/lib, which is linked in the wemd.py.
Data
To reproduce our results in the paper, we provide the processed testing samples from SDD [2] used in our paper. Please download them from [Link]
After extracting the datasets.zip, you will get a set of folders representing the testing scenes. For each scene you have the following structure:
- imgs: contains the images of the scene.
- floats: for each image, we store -features.float3 and -labels.float3 files. The former is a numpy array of shape (1154,5) which can store up to 1154 annotated objects. Each object has 5 components describing its bounding box (tl_x, tl_y, br_x, br_y, class_id). The indexes of the objects represent the tracking id and are given in the file -labels.float3.
- scene.txt: each line represent one testing sequence and has the following format: tracking_id img_0,img_1,img_2,img_future.
Additionally, we provide the processed training SDD which can be downloaded from [Link]
Models
We provide the final trained model for our EWTAD-MDF. Please download them from [Link]
Testing
To test our EWTAD-MDF, you can run:
python test.py --output
- --output: will write the output files to the disk under the path specified in the config.py (OUTPUT_FOLDER_FLN). If you need only to get the testing accuracies without writing files (much faster), you can simply remove the --output.
Training
We provide additionally the loss functions used when training our sampling-fitting network, please check the net.py file for more details.
CPI Dataset
We also provide the script to generate our CPI (Car Pedestrian Interaction) synthetic dataset. To generate the training dataset, you can run:
cd CPI/ python CPI-train.py output_folder n_scenes history n_gts dist
- output_folder: local folder where to store the generated dataset
- n_scenes: number of scenes to generate, where each scene correspond to one training sample (we use 20000)
- history: length of the history, which corresponds to the number of images used as input (we use 3)
- n_gts: number of ground truths of the future (we use 20)
- dist: the prediction horizon (we use 20)
Similarly, the testing dataset can be generated using:
python CPI-test.py cpi_testing_dataset 54 3 1000 20
Citation
If you use our repository or find it useful in your research, please cite the following paper:
@InProceedings{MICB19,
author = "O. Makansi and E. Ilg and {\"O}. {\c{C}}i{\c{c}}ek and T. Brox",
title = "Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction",
booktitle = "IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)",
month = " ",
year = "2019",
url = "http://lmb.informatik.uni-freiburg.de/Publications/2019/MICB19"
}
References
[1] S. Shirdhonkar and D. W. Jacobs. Approximate earth movers distance in linear time. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8, June 2008.
[2] A. Robicquet, A. Sadeghian, A. Alahi, S. Savarese, Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016.
License
This source code is shared under the license CC-BY-NC-SA, please refer to the LICENSE file for more information.
This source code is only shared for R&D or evaluation of this model on user database.
Any commercial utilization is strictly forbidden.
For any utilization with a commercial goal, please contact contact_cs or bendahan