[CVPRW 2024] Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large Vision Language Model

Khai Trinh Xuan, Khoi Nguyen Nguyen, Bach Hoang Ngo, Vu Dinh Xuan, Minh-Hung An, Quang-Vinh Dinh

⭐ The 2nd Place Solution to The 8th NVIDIA AI City Challenge (2024) Track 2 from AIO_ISC Team.

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Results

Rank	Team ID	Team name	MRR Score
1	208	AliOpenTrek	33.4308
2	28	AIO_ISC (Ours)	32.8877
3	68	Lighthouse	32.3006
4	87	VAI	32.2778
5	184	Santa Claude	29.7838

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Structure

Code Structure

├── src
│   ├── preprocess
│   |   ├── extract_frames
│   |   ├── segment_extraction
│   ├── train
│   |   ├── Qwen-VL
│   |   ├── prepare_train_data
│   ├── inference
│   ├── postprocess
│   ├── evaluation
├── tools
├── aux_dataset
│   ├── results
│   ├── submission
│   ├── train_data
│   ├── extracted_frames
│   ├── segmentation_data
├── dataset

Data Structure

Please download WTS dataset and set up the dataset as follow:

├── dataset
│   ├── annotations
│   |   ├── bbox_annotated
│   |   |   ├── pedestrian
│   |   |   |   ├── train
│   |   |   |   ├── val
│   |   |   |   ├── test
│   |   |   ├── vehicle
│   |   ├── bbox_generated
│   |   |   ├── ... (same structure)
│   |   ├── caption
│   |   |   |   ├── train
│   |   |   |   ├── val
│   |   |   |   ├── test
│   |   videos
│   |   |   ├── train
│   |   |   ├── val
│   |   |   ├── test
│   |   external
│   |   |   ├── BDD_PC_5K
│   |   |   |   ├── annotations
|   |   |   │   |   ├── bbox_annotated
|   |   │   |   |   |   ├── train
|   |   │   |   |   |   ├── val
|   |   │   |   |   |   ├── test
|   |   |   │   |   ├── bbox_generated
|   |   │   |   |   ├── ... (same structure)
|   |   |   │   |   ├── caption
|   |   │   |   |   ├── ... (same structure)
│   |   |   |   ├── videos
│   |   |   |   |   ├── train
│   |   |   |   |   ├── val
│   |   |   |   |   ├── test

Environment

pip install -r requirements.txt

Prepare

Run the following instructions to create our final submission or you can download our aux_dataset (not including extracted_frames) folder here. Inorder to run the repo, the user should use Nvidia GPU which has ampere architecture (rtx 3000 series, A5000, A6000,...) or higher (Hopper, Blackwell).

Preprocessing

Extracting video frames:

sh tools/extract_frames.sh

Segment Extraction: please create a new environment follow here and run:

sh tools/segment_extraction.sh

Remember to grant permisson to access mistralai/Mistral-7B-Instruct-v0.2 model on the huggingface hub.

Training

Prepare train data:

sh tools/prepare_train_data.sh

Training: Set the correct train and eval data path and run the code in here.

Inference

Inference trained model on test set:

sh tools/inference.sh

The pretrained checkpoints uploaded to huggingface hub are listed in here.

Postprocessing

sh tools/postprocess.sh

After run postprocessing, you can submit the file aux_dataset/submission.json to the official evaluation server.

Evaluation

We follow wts-dataset repo and reimplement the fast version at here.

Docker

We provide Dockerfile to build Segment Extraction environment :

sudo docker build -t segment_extraction .
sudo docker run -it --gpus all -v ./:/home/code -w /home/code segment_extraction

Citation

If you have any questions, please leave an issue or contact us: [email protected]

@InProceedings{Xuan_2024_CVPR,
    author    = {Xuan, Khai Trinh and Nguyen, Khoi Nguyen and Ngo, Bach Hoang and Xuan, Vu Dinh and An, Minh-Hung and Dinh, Quang-Vinh},
    title     = {Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large Vision Language Model},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2024},
    pages     = {7046-7055}
}

Acknowledgement

Our VLM training code relies on Qwen-VL repo. Thanks for their great work!