CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909371537031168 |
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| author | Desai, Nishq Poorav Etemad, Ali Greenspan, Michael |
| author_facet | Desai, Nishq Poorav Etemad, Ali Greenspan, Michael |
| contents | Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting 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. Next, we propose VidNeXt, a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash, we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/DeSinister/CycleCrash/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis Desai, Nishq Poorav Etemad, Ali Greenspan, Michael Computer Vision and Pattern Recognition Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting 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. Next, we propose VidNeXt, a novel method that leverages a ConvNeXt spatial encoder and a non-stationary transformer to capture the temporal dynamics of videos for the tasks defined in our dataset. To demonstrate the effectiveness of our method and create additional baselines on CycleCrash, we apply and compare 7 models along with a detailed ablation. We release the dataset and code at https://github.com/DeSinister/CycleCrash/ . |
| title | CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.19942 |