Small Vehicle Detector

Requirements

• Python 2.7.+

• TensorFlow v1.+

• OpenCV

Install

1. Clone the repository

git clone https://github.com/HCMUS-Smart-Environment-Group/small-vehicle-detector

2. Update your -arch in setup script to match your GPU

cd tf-faster-rcnn/lib
# Change the GPU architecture (-arch) if necessary
vim setup.py

GPU model	Architecture
TitanX (Maxwell/Pascal)	sm_52
GTX 960M	sm_50
GTX 1080 (Ti)	sm_61
Grid K520 (AWS g2.2xlarge)	sm_30
Tesla K80 (AWS p2.xlarge)	sm_37

Note: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs. Also even if you are only using CPU tensorflow, GPU based code (for NMS) will be used by default, so please set USE_GPU_NMS False to get the correct output.

3. Build the Cython modules

make clean
make
cd ..

4. Install the Python COCO API. The code requires the API to access COCO dataset.

cd data
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
make
cd ../../..

Download Pretrain Model

Link

Request Access is required. Contact email smartenvironment@selab.hcmus.edu.vn

Create mall-vehicle-detector/output/res101 folder

After extracting file, copy those files to small-vehicle-detector/output/res101

Demo

python tools/demo.py

Citations

Please consider citing this project in your publications if it helps your research:

@Inproceedings{nktuan-AICity2019,
  Title          = {Vehicle Re-identification with Learned Representation and Spatial Verification and Abnormality Detection with Multi-Adaptive Vehicle Detectors for Traffic Video Analysis},
  Author         = {Khac-Tuan Nguyen and Trung-Hieu Hoang and Minh-Triet Tran and Trung-Nghia Le and Ngoc-Minh Bui and Trong-Le Do and Viet-Khoa Vo-Ho and Quoc-An Luong and Mai-Khiem Tran and Thanh-An Nguyen and Thanh-Dat Truong and Vinh-Tiep Nguyen and Minh N. Do},
  BookTitle      = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  Year           = {2019},
}

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The code is used for academic purpose only.