| --- |
| 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 |
|
|
| [](https://huggingface.co/datasets/Yogesh914/RiskCueBench) |
|
|
| --- |
|
|
| ## 📖 Overview |
|
|
| RiskCueBench provides fine-grained annotations of **risk signals**—visual cues that precede potentially dangerous events. This dataset contains annotated video clips from two domains: **🚗 traffic accidents** and **📢 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 | |
| | 📝 | **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 | |
|
|
| ### 🎓 Evaluation Tasks |
|
|
| The dataset enables evaluation of models on: |
|
|
| - 🔍 **Risk Signal Detection** — Identifying visual cues that indicate potential danger |
| - ⏱️ **Temporal Reasoning** — Understanding the progression from risk signals to outcomes |
| - 🔄 **Cross-domain Generalization** — Testing on both traffic and social scenarios |
|
|
| --- |
|
|
| ## 📊 Dataset Statistics |
|
|
| | Split | Domain | Samples | Description | |
| |:------|:-------|:-------:|:------------| |
| | `carcrash` | 🚗 Traffic | **502** | Dashcam footage of driving scenarios | |
| | `protest` | 📢 Social Events | **484** | Protest and crowd footage | |
|
|
| --- |
|
|
| ## 📥 Downloading the Videos |
|
|
| ### 🚗 Car Crash Videos |
|
|
| The car crash videos are sourced from the **Car Crash Dataset (CCD)** published in ACM MM 2020. |
|
|
| **To download:** |
|
|
| 1. 🔗 Visit the official repository: **[CarCrashDataset](https://github.com/Cogito2012/CarCrashDataset)** |
| 2. 📦 Download the dataset from Google Drive (link provided in the repository) |
| 3. 🔢 The `video_id` column corresponds to the video filenames in the `Crash-1500` and `Normal` folders |
|
|
| <details> |
| <summary>📚 Citation for CCD</summary> |
|
|
| ```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} |
| } |
| ``` |
| </details> |
|
|
| ### 📢 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"` → [https://www.youtube.com/watch?v=5gM1gnMkUKU](https://www.youtube.com/watch?v=5gM1gnMkUKU) |
|
|
| 💡 **Tip:** Use tools like `yt-dlp` or `pytube` to download videos programmatically. |
|
|
| --- |
|
|
| ## 📋 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` | 👁️ Concise description of visual cues that signal potential risk | |
| | `riskSignalDescription` | `str` | 📝 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` | 💥 Frame when incident begins (`"/"` = no incident) | |
| | `accidentEndFrame` | `str` | 🏁 Frame when incident ends | |
| | `riskLabel` | `str` | 🏷️ `"yes"` = risk materializes, `"no"` = remains safe | |
|
|
| --- |
|
|
| ## 💻 Usage Example |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # 📦 Load the full dataset |
| dataset = load_dataset("Yogesh914/RiskCueBench") |
| |
| # 🔀 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") |
| |
| # 📄 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']}") |
| ``` |
|
|
| --- |
|
|
| ## 📝 Data Samples |
|
|
| ### 🚗 Car Crash Example (Risk = ✅ 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" |
| ``` |
|
|
| ### 📢 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" |
| ``` |
|
|
| ### 📢 Protest Example (Risk = ✅ 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" |
| ``` |
|
|
| --- |
|
|
| ## 📜 License |
|
|
| | Dataset | License | |
| |---------|---------| |
| | 🚗 Car Crash (CCD) | MIT License — [Original Repo](https://github.com/Cogito2012/CarCrashDataset) | |
| | 📢 Protest Videos | Subject to YouTube Terms of Service | |
|
|
| --- |
|
|
| ## 📚 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} |
| } |
| ``` |
|
|
| --- |
|
|
| <p align="center"> |
| Made with ❤️ for the research community |
| </p> |
