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From Sports to Safety: Benchmarking Proactive Risk Inference in MLLMs

Video Acquisition

We do not directly provide the original video files. Researchers can download the original videos using the video IDs provided in the annotation files, rename them to yt_{Video ID}.mp4, and then use the annotation files along with our provided script scripts/generate_clips.py to extract all video clips. Specific usage instructions are provided within the script code.

Video Annotations

The labels folder contains two video annotation files: labels_main.json includes annotations for 2,440 accident videos, and labels_safe.json includes annotations for 448 safe videos.

The annotation for each sample is formatted as follows:

    {
        "source_video": "yt_-8DwVUirUqE",               // "yt_" + Original video ID
        "filename": "yt_-8DwVUirUqE_1.mp4",             // Clip's filename
        "sport": {
            "type1": "Standardized Sports Venues",      // Environmental Setting
            "type2": "Cycling"                          // Sports Category
        },
        "hazard": {
            "level1": [
                "Personal Factors"                      // Macro Inducing Factors
            ],
            "level2": "Bicycle losing control after hitting a bump and crashing into a guardrail" // Direct Cause Description
        },
        "is_valid": true,
        "absolute_start_frame": 0,
        "absolute_end_frame": 212, // Annotation used for video cropping
        "is_cropped": true,
        "crop_box": {
            "x": 1,
            "y": 9,
            "w": 702,
            "h": 1254
        },
        "original_resolution": {
            "w": 720,
            "h": 1280
        },                         // Annotation used for video cropping
        "fps": 30.0,
        "timestamp1_frame_relative": 149, // Earliest Cue Moment
        "timestamp2_frame_relative": 167  // Most Obvious Moment
    }

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