This is the official repo for paper "Abductive Ego-View Accident Video Understanding for Safe Driving Perception"[CVPR2024 Highlight]

Paper MM-AU ｜ Project Homepage

• MM-AU Datasets
  • Introduction
  • Video_Metadata annotations
• Datasets Download
• Task & Benchmark
• Citation

 

 

We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descriptions. We annotate over 2.23 million object boxes and 58,650 pairs of video-based accident reasons, covering 58 accident categories. MM-AU consists of two datasets, LOTVS-Cap and LOTVS-DADA.

MM-AU is the first large-scale dataset for multi-modal accident video understanding for safe driving perception. It has the following highlights:

• first multi-modal accident video understanding benchmark in the safe driving field.
• MM-AU owns 11,727 in-the-wild ego-view accident videos.
• Each video is temporally aligned with the text descriptions of accident reasons, prevention solutions, and accident categories.
• 58.6K pairs of video-based accident reason answers (ArA) are annotated.
• Over 2.23 million object boxes are manually annotated for over 463K video frames.
• There are 58 accident categories are annotated, and the accident categories are determined by the participant-relations defined in DADA-2000.
• MM-AU facilitates 8 tasks of traffic accident video understanding (e.g. ① what objects are involved, ② what kinds of accidents, ③ where and ④ when the accident will occur, ⑤ why the accident occurs, ⑥ what are the keys to accident reasons, ⑦ how to prevent it, and ⑧ multimodal accident video diffusion). In addition, each task may be promoted by the developing of other related tasks.
• ONLY free for academic use. If you are interested for MM-AU,please contact [email protected]

An example:

{
"video_hashcode": {
        "video_name": "1_1",
        "id": "1",
        "type": "1",
        "weather": "1",
        "light": "1",
        "scenes": "4",
        "linear": "1",
        "accident occurred": "1",
        "abnormal_start_frame": "30",
        "abnormal_end_frame": "115",
        "accident_frame": "63",
        "total_frames": "440",
        "t_ai": "30",
        "t_co": "63",
        "t_ae": "115",
        "texts": "a pedestrian crosses the road",
        "causes": "Pedestrian does not notice the coming vehicles when crossing the street",
        "measures": "When passing the zebra crossing, drivers must slow down. When pedestrians or non-motor vehicles cross the zebra crossing, they should stop and give way to other normal running vehicles; When crossing the road, pedestrians must follow the zebra crossing, carefully observe the traffic, and do not cross the road in a hurry."
    }
}

Explanation:

• video_hashcode: Unique identifiers generated for all 11730 videos
• video_name: Consists of the type to which the video accident belongs and the serial number
• type: The type of the accident (you can find all the accident types in file)
• weather: sunny,rainy,snowy,foggy (1-4)
• light: day,night (1-2)
• scenes: highway,tunnel,mountain,urban,rural (1-5)
• linear: arterials,curve,intersection,T-junction,ramp (1-5)
• accident occurred: whether an accident occurred (1/0)
• t_ai: Accident window start frame
• t_co: Collision start frame
• t_ae: Accident window end frame
• texts: Description of the accident
• causes: Causes of the accident
• measures: Advice on how to avoid accident

the original raw datasets can be download here:

BaiDuNetDisk:link(we will reupload the raw data soon, please stay tuned)

You can download the training data of the Object Detection task in here

The raw data is like:

MM-AU # root of your MM-AU
├── CAP-DATA
│   ├── 1-10
│       ├── 1-10.zip
│       ├── 1-10.z01
│       ├── 1-10.z02
│   ├── 11
│   ├── 12-42
│   ├── 43
│   ├── 44-62
│   ├── cap_text_annotations.xls
├── DADA-DATA
│   ├── DADA-2000.zip
│   ├── DADA-2000.z01
│   ├── ......
│   ├── DADA-2000.z05
│   ├── dada_text_annotations.xlsx

Note: Due to the large amount of data, chunked compression is used. Please use windows decompression tool to decompress the data.

After decompression, please make the file structured as following:

MM-AU # root of your MM-AU
├── CAP-DATA
│   ├── 1-10
│       ├── 1
│           ├── 001537/images
│               ├── 000001.jpg
│               ├── ......
│       ├── 2
│       ├── ......
│       ├── 10
│   ├── 11
│   ├── 12-42
│   ├── 43
│   ├── 44-62
│   ├── cap_text_annotations.xls
├── DADA-DATA
│   ├── 1
│       ├── 001/images
│           ├── 0001.png
│           ├── ......
│   ├── 2
│   ├── ......
│   ├── 61
│   ├── dada_text_annotations.xlsx

MM-AU supports a variety of tasks due to its multimodal characteristics, and the following describes the application of MM-AU to various tasks.

• Object Detection(Click to open the doc of OD task)
• Ego-view Accident Reason Answering
• Ego-view Accident Video Diffusion
• Vision-based Accident Anticipation
• Ego-view Accident Vide Classification(Coming soon)
• Ego-view Accident Video Detection
• Causal Inference for Accident Prevention
• Driver Attention Prediction

If our work and repo is helpful to you, please cite our paper,give us a free star and sign up on our homepape, thanks!

@InProceedings{Fang_2024_CVPR,
    author    = {Fang, Jianwu and Li, Lei-lei and Zhou, Junfei and Xiao, Junbin and Yu, Hongkai and Lv, Chen and Xue, Jianru and Chua, Tat-Seng},
    title     = {Abductive Ego-View Accident Video Understanding for Safe Driving Perception},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {22030-22040}
}