--- license: apache-2.0 configs: - config_name: default data_files: - split: carcrash path: data/carcrash-* - split: protest path: data/protest-* dataset_info: features: - name: video_id dtype: string - name: riskVisualIndicator dtype: string - name: riskSignalDescription dtype: string - name: riskSignalStart dtype: string - name: riskSignalEnd dtype: string - name: accidentStartFrame dtype: string - name: accidentEndFrame dtype: string - name: riskLabel dtype: string splits: - name: carcrash num_bytes: 79399 num_examples: 502 - name: protest num_bytes: 103623 num_examples: 484 download_size: 76908 dataset_size: 183022 task_categories: - video-classification - video-text-to-text language: - en size_categories: - n<1K --- # 🚨 RiskCueBench > A benchmark dataset for evaluating risk reasoning capabilities in video understanding models [![Dataset on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md.svg)](https://huggingface.co/datasets/Yogesh914/RiskCueBench) --- ## πŸ“– Overview RiskCueBench provides fine-grained annotations of **risk signals**β€”visual cues that precede potentially dangerous events. This dataset contains annotated video clips from two domains: **πŸš— traffic accidents** and **πŸ“’ protest events**, designed to test temporal risk anticipation and visual reasoning. ### ✨ Key Features | | Feature | Description | |---|---------|-------------| | 🎯 | **Risk Signal Annotations** | Temporal boundaries marking when risk indicators appear | | πŸ“ | **Rich Descriptions** | Detailed narratives of visual cues and event progressions | | 🏷️ | **Binary Labels** | Clear yes/no labels for whether risk materializes | | 🌐 | **Cross-domain** | Two distinct domains for generalization testing | ### πŸŽ“ Evaluation Tasks The dataset enables evaluation of models on: - πŸ” **Risk Signal Detection** β€” Identifying visual cues that indicate potential danger - ⏱️ **Temporal Reasoning** β€” Understanding the progression from risk signals to outcomes - πŸ”„ **Cross-domain Generalization** β€” Testing on both traffic and social scenarios --- ## πŸ“Š Dataset Statistics | Split | Domain | Samples | Description | |:------|:-------|:-------:|:------------| | `carcrash` | πŸš— Traffic | **502** | Dashcam footage of driving scenarios | | `protest` | πŸ“’ Social Events | **484** | Protest and crowd footage | --- ## πŸ“₯ Downloading the Videos ### πŸš— Car Crash Videos The car crash videos are sourced from the **Car Crash Dataset (CCD)** published in ACM MM 2020. **To download:** 1. πŸ”— Visit the official repository: **[CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)** 2. πŸ“¦ Download the dataset from Google Drive (link provided in the repository) 3. πŸ”’ The `video_id` column corresponds to the video filenames in the `Crash-1500` and `Normal` folders
πŸ“š Citation for CCD ```bibtex @InProceedings{BaoMM2020, author = {Bao, Wentao and Yu, Qi and Kong, Yu}, title = {Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning}, booktitle = {ACM Multimedia Conference}, year = {2020} } ```
### πŸ“’ Protest Videos The protest videos are sourced from **YouTube**. The `video_id` column contains the YouTube video ID. **URL Format:** ``` https://www.youtube.com/watch?v={video_id} ``` **Example:** For `video_id = "5gM1gnMkUKU"` β†’ [https://www.youtube.com/watch?v=5gM1gnMkUKU](https://www.youtube.com/watch?v=5gM1gnMkUKU) πŸ’‘ **Tip:** Use tools like `yt-dlp` or `pytube` to download videos programmatically. --- ## πŸ“‹ Column Descriptions | Column | Type | Description | |:-------|:-----|:------------| | `video_id` | `str` | 🎬 Unique identifier for the video. For car crash: filename ID from CCD. For protest: YouTube video ID. | | `riskVisualIndicator` | `str` | πŸ‘οΈ Concise description of visual cues that signal potential risk | | `riskSignalDescription` | `str` | πŸ“ Detailed narrative of what happens during the risk signal period | | `riskSignalStart` | `str` | ⏱️ Frame marking the **beginning** of the risk signal | | `riskSignalEnd` | `str` | ⏱️ Frame marking the **end** of the risk signal | | `accidentStartFrame` | `str` | πŸ’₯ Frame when incident begins (`"/"` = no incident) | | `accidentEndFrame` | `str` | 🏁 Frame when incident ends | | `riskLabel` | `str` | 🏷️ `"yes"` = risk materializes, `"no"` = remains safe | --- ## πŸ’» Usage Example ```python from datasets import load_dataset # πŸ“¦ Load the full dataset dataset = load_dataset("Yogesh914/RiskCueBench") # πŸ”€ Access specific splits carcrash_data = dataset["carcrash"] protest_data = dataset["protest"] # 🎯 Filter for risky scenarios risky_carcrash = carcrash_data.filter(lambda x: x["riskLabel"] == "yes") risky_protest = protest_data.filter(lambda x: x["riskLabel"] == "yes") # πŸ“„ Access a sample sample = carcrash_data[0] print(f"Video ID: {sample['video_id']}") print(f"Risk Signal: {sample['riskSignalDescription']}") print(f"Risk Label: {sample['riskLabel']}") ``` --- ## πŸ“ Data Samples ### πŸš— Car Crash Example (Risk = βœ… Yes) ```yaml video_id: 1 riskVisualIndicator: "Black car, intersection" riskSignalDescription: "First, a black-colored car enters the intersection against the traffic, then it continues into the driver's path." riskSignalStart: 23 # frame number riskSignalEnd: 31 # frame number accidentStartFrame: 32 # frame number accidentEndFrame: 50 riskLabel: "yes" ``` ### πŸ“’ Protest Example (Risk = ❌ No) ```yaml video_id: "5gM1gnMkUKU" riskVisualIndicator: "Police body language, repeated hand gesture" riskSignalDescription: "First a group of geared police show up, then one police use subtle body language to express message seem to be 'come here', and did it twice." riskSignalStart: "00:00" # MM:SS format riskSignalEnd: "00:13" # MM:SS format accidentStartFrame: "/" # no incident accidentEndFrame: "/" riskLabel: "no" ``` ### πŸ“’ Protest Example (Risk = βœ… Yes) ```yaml video_id: "m6CwOP4rUAo" riskVisualIndicator: "Police tank appears, gun raised, aiming gesture" riskSignalDescription: "First a few police on police tank show up, then the police tank raise gun up and target at the protestors" riskSignalStart: "00:17" # MM:SS format riskSignalEnd: "00:18" # MM:SS format accidentStartFrame: "00:19" accidentEndFrame: "00:25" riskLabel: "yes" ``` --- ## πŸ“œ License | Dataset | License | |---------|---------| | πŸš— Car Crash (CCD) | MIT License β€” [Original Repo](https://github.com/Cogito2012/CarCrashDataset) | | πŸ“’ Protest Videos | Subject to YouTube Terms of Service | --- ## πŸ“š Citation If you use this dataset, please cite: ```bibtex @dataset{riskcuebench2025, title = {RiskCueBench: A Benchmark for Video Risk Reasoning}, author = {[Authors]}, year = {2025}, publisher = {}, url = {https://huggingface.co/datasets/Yogesh914/RiskCueBench} } ``` Additionally, please cite the Car Crash Dataset if you use the `carcrash` split: ```bibtex @InProceedings{BaoMM2020, author = {Bao, Wentao and Yu, Qi and Kong, Yu}, title = {Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning}, booktitle = {ACM Multimedia Conference}, year = {2020} } ``` ---

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