TTHF

The code of Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos Rongqin Liang, Yuanman Li, Jiantao Zhou, and Xia Li

Our TTHF framwork:

Installation

Dependencies

• Python 3.8
• pytorch 2.0.1
• cuda 11.3
• Ubuntu 20.04
• RTX 3090
• Please refer to the "requirements.txt" file for more details.

Preparing

First, prepare the data for training or testing by:

cd TTHF/datasets
python ./extract_samples.sh

Note that you need to modify the corresponding path.

Training

Users can train the TTHF models on DoTA dataset easily by runing the following command:

cd TTHF
python3 ./main.py \
        --train \
        --lr_clip 5e-6 \
        --wd 1e-4 \
        --epochs 15 \
        --batch_size 128 \
        --dataset DoTA \
        --gpu_num 0 \
        --height 224 \
        --width 224 \
        --normal_class 1 \
        --eval_every 1000 \
        --base_model 'RN50' \
        --general \
        --fg \
        --hf \
        --aafm \
        --other_method 'TDAFF_BASE' \
        --exp_name 'TDAFF_BASE_RN50'

Note that you need to modify the corresponding path.

Inference

Users can test the TTHF models (Extraction code: rb9k) on DoTA or DADA-2000 dataset easily by runing the following command:

For DoTA:

python3 ./main.py \
        --evaluate \
        --batch_size 128 \
        --dataset DoTA \
        --gpu_num 0 \
        --height 224 \
        --width 224 \
        --normal_class 1 \
        --eval_every 1000 \
        --base_model 'RN50' \
        --general \
        --fg \
        --hf \
        --aafm \
        --other_method 'TDAFF_BASE' \
        --exp_name 'TDAFF_BASE_RN50'

For DADA-2000:

python3 ./main.py \
        --evaluate \
        --batch_size 128 \
        --dataset DADA \
        --gpu_num 0 \
        --height 224 \
        --width 224 \
        --normal_class 1 \
        --eval_every 1000 \
        --base_model 'RN50' \
        --general \
        --fg \
        --hf \
        --aafm \
        --other_method 'TDAFF_BASE' \
        --exp_name 'TDAFF_BASE_RN50'

User can also see "tdaff_base_script.sh" for more training and testing commands.

Note that our project is developed based on the code of Learning Transferable Visual Models From Natural Language Supervision. The relevant pre-trained models can be downloaded from the official website.

Citation

If you found the repo is useful, please feel free to cite our papers:

@article{10504300,
    title={Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos},
    author={Rongqin Liang and Yuanman Li and Jiantao Zhou and Xia Li},
    journal={IEEE Transactions on Circuits and Systems for Video Technology},
    year={2024},
    doi={10.1109/TCSVT.2024.3390173}
}