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

Related tags

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}
}
Owner
GitHub Repository
Improving Calibration for Long-Tailed Recognition (CVPR2021)

MiSLAS Improving Calibration for Long-Tailed Recognition Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia [arXiv] [slide] [BibTeX] Introductio

DV Lab 116 Dec 20, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

Awesome Machine Learning Jupyter Notebooks for Google Colaboratory A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook

Carlos Toxtli 245 Jan 01, 2023
Simulated garment dataset for virtual try-on

Simulated garment dataset for virtual try-on This repository contains the dataset used in the following papers: Self-Supervised Collision Handling via

33 Dec 20, 2022
Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

Ruiqi Gao 39 Nov 10, 2022
Privacy-Preserving Machine Learning (PPML) Tutorial Presented at PyConDE 2022

PPML: Machine Learning on Data you cannot see Repository for the tutorial on Privacy-Preserving Machine Learning (PPML) presented at PyConDE 2022 Abst

Valerio Maggio 10 Aug 16, 2022
Official implementation for the paper "Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection"

Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection PyTorch code release of the paper "Attentive Prototypes for Sour

Deepti Hegde 23 Oct 17, 2022
Emotional conditioned music generation using transformer-based model.

This is the official repository of EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has b

hung anna 96 Nov 09, 2022
A highly efficient, fast, powerful and light-weight anime downloader and streamer for your favorite anime.

AnimDL - Download & Stream Your Favorite Anime AnimDL is an incredibly powerful tool for downloading and streaming anime. Core features Abuses the dev

KR 759 Jan 08, 2023
Classify the disease status of a plant given an image of a passion fruit

Passion Fruit Disease Detection I tried to create an accurate machine learning models capable of localizing and identifying multiple Passion Fruits in

3 Nov 09, 2021
Demo project for real time anomaly detection using kafka and python

kafkaml-anomaly-detection Project for real time anomaly detection using kafka and python It's assumed that zookeeper and kafka are running in the loca

Rodrigo Arenas 36 Dec 12, 2022
This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

PyTorch Infer Utils This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model infer

Alex Gorodnitskiy 11 Mar 20, 2022
Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Noisy Natural Gradient as Variational Inference PyTorch implementation of Noisy Natural Gradient as Variational Inference. Requirements Python 3 Pytor

Tony JiHyun Kim 119 Dec 02, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Python implementation of Bayesian optimization over permutation spaces.

Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o

Aryan Deshwal 9 Dec 23, 2022
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021)

HAIS Hierarchical Aggregation for 3D Instance Segmentation (ICCV 2021) by Shaoyu Chen, Jiemin Fang, Qian Zhang, Wenyu Liu, Xinggang Wang*. (*) Corresp

Hust Visual Learning Team 145 Jan 05, 2023
AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

AT-BMC Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper Prerequisites Install pac

16 Nov 26, 2022
A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking PoseRBPF Paper Self-supervision Paper Pose Estimation Video Robot Manipulati

NVIDIA Research Projects 107 Dec 25, 2022
PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning Warning: This is a rapidly evolving research prototype.

MIT Probabilistic Computing Project 190 Dec 27, 2022