CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis

This paper has been accepted to WACV 2025

Nishq Poorav Desai   Ali Etemad   Michael Greenspan

[Paper] [Appendix] [ArXiv] [Website]

Overview

This repo contains the CycleCrash dataset and the code for our [WACV2025 paper] on bicycle collision prediction and analysis.

The dataset consists of 3,000 dashcam videos with 436,347 frames that capture cyclists in a range of critical situations, from collisions to safe interactions. This dataset enables 9 different cyclist collision prediction and classification tasks focusing on potentially hazardous conditions for cyclists and is annotated with collision-related, cyclist-related, and scene-related labels. Please refer to the dataset.csv file for detailed annotations and additional information about each video in the dataset.

This code also contains PyTorch implementation for VidNeXt, a novel method that uses a non-stationary transformer on the defined tasks within our dataset, along with 7 baseline models.

Files

• dataset.csv: CSV file containing the CycleCrash dataset.
• download: This folder contains the source code for downloading and preparing the CycleCrash dataset.
• download_dataset.py: Python script to download the videos of the dataset.
• preprocess.py: Python script to Pre-process videos for background cropping and uniform temporal and spatial dimensions.
• src: This folder contains the source code for executing the VidNeXt and baseline models on the tasks of CycleCrash dataset.
• data_loader.py: Python script for implementing PyTorch-based Data loader for CycleCrash dataset
• get_model.py: Python script to load the baseline models, the proposed VidNeXt and its ablation variants.
• get_model.py: Python script to run the training and evaluation of the proposed VidNeXt and its ablation variants on CycleCrash dataset.

Preparing the dataset

1. Clone the Github repository:

   • Run the following command in a terminal to clone the GitHub repository in your current location.

   git clone https://github.com/DeSinister/CycleCrash.git
   cd CycleCrash
   

2. Installing Packages

   • Run the following command to make sure the necessary packages are installed.

   pip install -r requirements.txt
   

3. FFmpeg:

   • Download the full build of FFmpeg from FFmpeg Releases.
   • Extract the downloaded ZIP file to a location on your system, e.g., C:\ffmpeg-6.0-full_build.

4. yt-dlp:

   • Download yt-dlp from the yt-dlp GitHub repository.
   • Place the yt-dlp.exe in the bin directory of the FFmpeg folder, e.g., C:\ffmpeg-6.0-full_build\ffmpeg-6.0-full_build\bin.

5. Downloading the dataset

   • Set the paths for the dataset.csv, output folder, and folder path containing yt-dlp.exe in download_dataset.py.
   • Run the python file download.py to collect the videos.

   python download/download_dataset.py
   

6. Preprocessing the videos

   • Set the paths for the dataset.csv, the directory where videos are stored, and the output directory in pre_process.py
   • Run the preprocess.py file.

   python download/preprocess.py
   

Training VidNeXt

• Run src/main.py with the required hyperparameter setting in arguments.

python src/main.py -vid_dir PATH_TO_PREPROCESSED_VIDEOS -csv_file PATH_TO_DATASET_CSV -task TASK_TYPE 

Results

Method	Risk		Right-of-way		Collision		Time-to-coll.	Severity		Fault		Age		Direction		Object
	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑	MSE ↓	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑	Acc. ↑	F1 ↑
	TimeSformer	65.74	41.79	60.20	55.71	66.45	69.69	1.41	36.49	23.01	59.65	51.03	93.77	66.68	47.19	31.38	45.02	29.00
ViViT	65.12	39.06	52.84	53.74	57.01	69.92	1.33	47.51	24.47	53.37	50.42	93.56	66.34	36.29	27.99	46.30	26.34
ResNet50 3D	65.76	39.53	59.41	53.97	63.10	60.24	1.38	56.60	26.12	59.37	54.91	94.21	54.86	46.30	30.12	43.27	27.77
R(2+1)D	66.54	39.56	60.31	53.42	67.71	63.33	1.43	56.63	25.46	50.53	52.62	94.41	53.24	47.49	30.36	40.75	25.48
X3D-M	64.76	38.75	59.83	57.57	63.72	61.08	1.44	54.45	24.70	52.16	52.19	94.34	53.78	47.82	31.85	42.72	23.79
X3D-S	63.37	36.28	60.10	56.90	61.49	61.13	1.47	51.80	24.09	60.47	51.88	94.38	54.30	45.62	30.05	42.03	22.38
X3D-XS	64.77	37.23	59.37	53.43	60.59	60.73	1.47	51.39	23.77	56.10	52.59	93.87	52.37	46.77	30.22	41.73	26.57
ConvNeXt+VT	64.89	40.05	61.13	54.00	63.50	65.06	1.56	53.80	26.54	56.74	55.72	94.55	66.78	46.46	32.62	42.85	25.16
ResNet+NST	67.18	40.74	61.77	58.62	60.79	62.28	1.39	53.88	24.67	57.17	54.08	94.52	53.49	45.12	28.48	44.17	26.91
VidNeXt (Ours)	66.20	41.96	64.28	57.51	64.84	70.84	1.38	59.66	31.78	65.16	52.51	94.57	67.88	47.94	31.20	42.31	28.37

Combined experimental results for tasks 1 through 9. The methods include the 7 baselines based on prior works, 2 ablation variants, and VidNeXt.

Citation

If you find this repository useful, please consider giving it a star ⭐ and citing the paper:

@inproceedings{desai2025cyclecrash,
  title={CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis},
  author={Desai, Nishq Poorav and Etemad, Ali and Greenspan, Michael},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  year={2025}
}

Acknowledgements

This project includes code borrowed from the following repositories:

• ViViT by Google Research.
• Non-stationary Trasnformer.
• ConvNeXt by Facebook Research.
• TimeSformer by Facebook Research.
• 3D ResNet, R(2+1)D, X3D by Facebook Research.