Awesome
ELECTRICITY: An Efficient Multi-camera Vehicle Tracking System for Intelligent City
Authors: Yijun Qian, Lijun Yu, Wenhe Liu, Alexander G Hauptmann
Email: yijunqia@andrew.cmu.edu, lijun@cmu.edu
@InProceedings{Qian_2020_CVPR_Workshops,
author = {Qian, Yijun and Yu, Lijun and Liu, Wenhe and Hauptmann, Alexander G.},
title = {ELECTRICITY: An Efficient Multi-Camera Vehicle Tracking System for Intelligent City},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
Overview
We release the code for our winnning model on AI City 2020 Challenge (https://www.aicitychallenge.org/) Track 3. For more information please refer to our accepted paper in CVPR 2020 AI City Workshop.
Project Download
Firstly please download the project through:
git clone https://github.com/KevinQian97/ELECTRICITY-MTMC.git
Prerequisites
The code is built with many libraries, we have listed the official sites of part of them. If you encounter problems about the dependencies, please resort to these official sites for help.
We have prepared the environment config file and suggest build the environment through ANACONDA.
cd ELECTRICITY-MTMC
conda env create -f environment.yml
conda activate aic20_track3
Data Preparation
If you want to reproduce our results on AI City Challenge or train the model by yourself, please download the data set from: (https://www.aicitychallenge.org/2020-data-and-evaluation/) and put it under the folder datasets. Make sure the data structure is like:
- ELECTRICITY-MTMC
- datasets
- aic_20_trac3
- test (test folder)
- eval
- validation (validation folder)
- cam_timestamp
- cam_loc
- cam_framenum
- train (train folder)
- aic_20_trac3
- datasets
Pretrained Models
We also provided the pretrained model: Notice: The accuracy and map here is calculated on our inner split of validation set. The submission model is trained on both train and validation sets.
model | Acc 1 | MAP | Epochs | Linkage |
---|---|---|---|---|
Agg_ResNet101 | 92.0% | 82.3% | 10 | link |
Inference
If you just want inference or reproduce our results, you can directly download our pretrained model and:
cd ELECTRICITY-MTMC
mkdir models
cd models
mkdir resnet101-Aic
Then put the pretrained model under this folder and run:
cd ELECTRICITY-MTMC
bash test.sh
The final results will locate at path ./exp/track3.txt
Training
If you want to train the model by yourself, please first generate training sets through:
bash ./prepare.sh
Then run:
bash ./train.sh
You will get trained model under path ./models/resnet101-Aic Finally run:
bash ./test.sh
The final results will locate at path ./exp/track3.txt
Performance
The speed is tested on four 2080Ti GPUs.
- Speed: 0.345x real-time (it means we only need 0.345 second to tackle 1 second video)
- IDF1: 0.4616
License
See LICENSE
. Please read before use.