Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

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Deep LearningTraffic4D-Release
Overview

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Project | PDF | Poster
Fangyu Li, N. Dinesh Reddy, Xudong Chen and Srinivasa G. Narasimhan
Proceedings of IEEE Intelligent Vehicles Symposium (IV'21)
Best Paper Award

Following instructions below, the user will get keypoints, trajectory reconstruction and vehicular activity clustering results like

Set up

The set up process can be skipped if using docker. Please check "Docker" section.

Python

Python version 3.6.9 is used. Python packages are in requirements.txt .

git clone https://github.com/Emrys-Lee/Traffic4D-Release.git
sudo apt-get install python3.6
sudo apt-get install python3-pip
cd Traffic4D-Release
pip3 install -r requirements.txt

C++

Traffic4D uses C++ libraries ceres and pybind for efficient optimization. pybind needs clang compiler, so Traffic4D uses clang compiler.

Install clang compiler

sudo apt-get install clang++-6.0

Install prerequisites for ceres

# CMake
sudo apt-get install cmake
# google-glog + gflags
sudo apt-get install libgoogle-glog-dev libgflags-dev
# BLAS & LAPACK
sudo apt-get install libatlas-base-dev
# Eigen3
sudo apt-get install libeigen3-dev
# SuiteSparse and CXSparse (optional)
sudo apt-get install libsuitesparse-dev

Download and install ceres

wget https://github.com/ceres-solver/ceres-solver/archive/1.12.0.zip
unzip 1.12.0.zip
cd ceres-solver-1.12.0/
mkdir build
cd build
cmake ..
make
sudo make install

Download and install pybind

git clone https://github.com/pybind/pybind11
cd pybind11
cmake .
make
sudo make install

Build Traffic4D optimization library

cd Traffic4D-Release/src/ceres
make

ceres_reconstruct.so and ceres_spline.so are generated under path Traffic4D-Release/src/ceres/.

Dataset

Download dataset and pre-generated results from here, and put it under Traffic4D-Release/.

cd Traffic4D-Release
mv Data-Traffic4D.zip ./
unzip Data-Traffic4D.zip

The directory should be like

Traffic4D-Release/
    Data-Traffic4D/
    └───fifth_morewood/
        └───fifth_morewood_init.vd
        └───top_view.png
        └───images/
                00001.jpg
                00002.jpg
                ...
                06288.jpg
    └───arterial_kennedy/
        └───arterial_kennedy_init.vd
        └───top_view.png
        └───images/
                <put AI City Challenge frames here>
        ...

The input and output paths can be modified in config/*.yml.

Explanation

1. Input videos

Sample videos in Traffic4D are provided. Note arterial_kennedy and dodge_century are from Nvidia AI City Challenge City-Scale Multi-Camera Vehicle Tracking Challenge Track. Please request the access to the dataset here. Once get the data, run

ffmpeg -i <mtmc-dir>/train/S01/c001/vdo.avi Traffic4D-Release/Data-Traffic4D/arterial_kennedy/images/%05d.jpg
ffmpeg -i <mtmc-dir>/test/S02/c007/vdo.avi Traffic4D-Release/Data-Traffic4D/dodge_century/images/%05d.jpg

to extract frames into images/.

2. Pre-Generated 2D results

Detected 2D bounding boxes, keypoints and tracking IDs are stored in *_init.vd. Check Occlusionnet implementation for detecting keypoints; V-IOU for multi-object tracking.

3. Output folder

Folder Traffic4D-Release/Result/ will be created by default.

Experiments

Run python exp/traffic4d.py config/<intersection_name>.yml <action>. Here YML configuration files for multiple intersections are provided under config/ folder. <action> shoulbe be reconstruction or clustering to perform longitudinal reconstruction and activity clustering sequentially. For example, below runs Fifth and Morewood intersection.

cd Traffic4D-Release
python3 exp/traffic4d.py config/fifth_morewood.yml reconstruction
python3 exp/traffic4d.py config/fifth_morewood.yml clustering

Results

Find these results in the output folder:

  1. 2D keypoints: If 3D reconstruction is done, 2D reprojected keypoints will be plotted in Traffic4D-Release/Result/<intersection_name>_keypoints/.
  2. 3D reconstructed trajectories and clusters: The clustered 3D trajectories are plotted on the top view map as Traffic4D-Release/Result/<intersection_name>_top_view.jpg.

Docker

We provide docker image with dependencies already set up. The steps in "Set up" can be skipped if you use docker image. You still need to clone the repo and download the dataset and put it in under Traffic4D-Release/.

git clone https://github.com/Emrys-Lee/Traffic4D-Release.git

Pull Traffic4D docker image.

docker pull emrysli/traffic4d-release:latest

Then create a container and map the git repo into docker container to access the dataset. For example, if the cloned repo locates at host directory /home/xxx/Traffic4D-Release, <path_to_repo> should be /home/xxx. If <path_in_container> is /home/yyy, then /home/xxx/Traffic4D-Release will be mapped as /home/yyy/Traffic4D-Release inside the container.

docker run -it -v <path_to_repo>/Traffic4D-Release:<path_in_container>/Traffic4D-Release emrysli/traffic4d-release:latest /bin/bash

Inside container compile Traffic4D again.

# inside container
cd <path_in_container>/Traffic4D-Release/src/ceres
make

Run experiments.

cd <path_in_container>/Traffic4D-Release
python3 exp/traffic4d.py config/fifth_morewood.yml reconstruction
python3 exp/traffic4d.py config/fifth_morewood.yml clustering

Trouble Shooting

  1. tkinter module is missing
File "/usr/local/lib/python3.6/dist-packages/matplotlib/backends/_backend_tk.py", line 5, in <module>
    import tkinter as Tk
ModuleNotFoundError: No module named 'tkinter'

Solution: install tkinter.

sudo apt-get install python3-tk
  1. opencv import error such as
File "/usr/local/lib/python3.6/dist-packages/cv2/__init__.py", line 3, in <module>
    from .cv2 import *
ImportError: libSM.so.6: cannot open shared object file: No such file or directory

Solution: install the missing libraries.

sudo apt-get install libsm6 libxrender1 libfontconfig1 libxext6

Citation

Traffic4D

@conference{Li-2021-127410,
author = {Fangyu Li and N. Dinesh Reddy and Xudong Chen and Srinivasa G. Narasimhan},
title = {Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision},
booktitle = {Proceedings of IEEE Intelligent Vehicles Symposium (IV '21)},
year = {2021},
month = {July},
publisher = {IEEE},
keywords = {Self-Supervision, vehicle Detection, 4D Reconstruction, 3D reconstuction, Pose Estimation.},
}

Occlusion-Net

@inproceedings{onet_cvpr19,
author = {Reddy, N. Dinesh and Vo, Minh and Narasimhan, Srinivasa G.},
title = {Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {7326--7335},
year = {2019}
}
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