Based on insights from previous video-based challenges (NFL Contact & Impact Detection, YouTube-8M, ASL Fingerspelling, and Isolated Sign Language Recognition) I have a discussion post on it that you can check out and the complete EDA performed (you can find my public notebook also), here are some ideas on how we can tackle the Crash Prediction competition:
\nData Preprocessing & EDA
\nModel Architecture
\nAugmentation & Regularization
\nPostprocessing & Ensembling
\nInspiration from Previous Competitions
\nFinal Thoughts
\nFocusing on precise temporal localization, robust data augmentation, and hybrid spatio-temporal modeling is key. By integrating techniques from these past competitions and carefully designing our ensembling and postprocessing steps, we can create a powerful model for early crash prediction.
Happy Kaggling – let’s push the boundaries in accident prediction!
", "messages": [ { "id": "3132102", "postDate": "02/23/2025 18:04:34", "content": "Based on insights from previous video-based challenges (NFL Contact & Impact Detection, YouTube-8M, ASL Fingerspelling, and Isolated Sign Language Recognition) I have a discussion post on it that you can check out and the complete EDA performed (you can find my public notebook also), here are some ideas on how we can tackle the Crash Prediction competition:
\nData Preprocessing & EDA
\nModel Architecture
\nAugmentation & Regularization
\nPostprocessing & Ensembling
\nInspiration from Previous Competitions
\nFinal Thoughts
\nFocusing on precise temporal localization, robust data augmentation, and hybrid spatio-temporal modeling is key. By integrating techniques from these past competitions and carefully designing our ensembling and postprocessing steps, we can create a powerful model for early crash prediction.
Happy Kaggling – let’s push the boundaries in accident prediction!
", "rawMarkdown": "Based on insights from previous video-based challenges (NFL Contact & Impact Detection, YouTube-8M, ASL Fingerspelling, and Isolated Sign Language Recognition) I have a discussion post on it that you can check out and the complete EDA performed (you can find my public notebook also), here are some ideas on how we can tackle the Crash Prediction competition:\n\n---\n\n**Data Preprocessing & EDA** \n- **CSV & Video Analysis:** \n - Leverage metadata (e.g., time-of-event, time-of-alert) to understand collision timing.\n - Extract key frames from dashcam videos, noting that most collisions occur mid-video (~40 sec).\n- **Normalization & Augmentation:** \n - Standardize frame resolution (1280×720) and adjust for varying brightness.\n - Apply temporal sampling (selecting frames before the collision event) and spatial cropping around vehicles.\n\n---\n\n**Model Architecture** \n- **3D CNN Approaches:** \n - Inspired by NFL solutions, use 3D CNNs (e.g., resnet50-irCSN) to capture spatio-temporal cues from video sequences.\n - Experiment with multi-view inputs if multiple dashcam angles are available.\n- **Transformer & Hybrid Models:** \n - Combine CNN-extracted features with Transformer layers to capture long-range temporal dependencies.\n - Consider hybrid architectures (e.g., CNN + RNN/Transformer) similar to approaches in YouTube-8M and ASL tasks.\n- **Optical Flow & Motion Features:** \n - Integrate optical flow data to better capture vehicle motion dynamics and predict imminent collisions.\n\n---\n\n**Augmentation & Regularization** \n- **Temporal Augmentations:** \n - Apply random time-warping, frame dropping, and shifting to simulate different collision timings.\n- **Spatial Augmentations:** \n - Use affine transformations (rotation, scaling, shifting) and brightness/contrast adjustments to improve robustness under varied weather and lighting.\n- **Regularization Techniques:** \n - Utilize heavy dropout, stochastic depth, and adversarial weight perturbation (AWP) to prevent overfitting.\n\n---\n\n**Postprocessing & Ensembling** \n- **Temporal Smoothing:** \n - Apply sliding-window filtering or temporal NMS to smooth predictions across adjacent frames.\n- **Ensembling:** \n - Blend multiple model outputs (e.g., 3D CNN, Transformer-based, hybrid models) using simple averaging or an XGBoost-based postprocessor to improve robustness.\n\n---\n\n**Inspiration from Previous Competitions** \n- **NFL Solutions:** \n - Multi-view processing and the use of tracking data can be adapted to fuse different dashcam perspectives and sensor inputs.\n- **YouTube-8M Strategies:** \n - Candidate generation followed by segment-level re-ranking can be adapted to localize early crash events.\n- **ASL Recognition Methods:** \n - Robust preprocessing (normalization, landmark extraction) and creative augmentations (CutMix, time masking) can inspire effective feature extraction from dashcam footage.\n\n---\n\n**Final Thoughts** \nFocusing on precise temporal localization, robust data augmentation, and hybrid spatio-temporal modeling is key. By integrating techniques from these past competitions and carefully designing our ensembling and postprocessing steps, we can create a powerful model for early crash prediction.\n\nHappy Kaggling – let’s push the boundaries in accident prediction!", "votes": null }, { "id": "3132186", "postDate": "02/23/2025 20:36:03", "content": "Great thoughts and insights!
", "rawMarkdown": "Great thoughts and insights!", "votes": null }, { "id": "3136460", "postDate": "02/28/2025 14:45:42", "content": "Great insights!
\nI am interested to integrate optical flow data, and need videos of optical flow (both dense and sparse) calculated from train and test dataset videos. Does anybody have those videos online?
", "rawMarkdown": "Great insights! \n\nI am interested to integrate optical flow data, and need videos of optical flow (both dense and sparse) calculated from train and test dataset videos. Does anybody have those videos online?", "votes": null }, { "id": "3152990", "postDate": "03/18/2025 10:33:03", "content": "Great post! One question though, is it really possible to use videos with 1280×720 resolution? When I allocate the videos to my GPU it runs out of memory.
", "rawMarkdown": "Great post! One question though, is it really possible to use videos with 1280×720 resolution? When I allocate the videos to my GPU it runs out of memory.", "votes": null }, { "id": "3153017", "postDate": "03/18/2025 11:06:38", "content": "You can try resizing the videos to something lesser. I have tried 224 X 224 in this notebook
", "rawMarkdown": "You can try resizing the videos to something lesser. I have tried 224 X 224 in this [notebook](https://www.kaggle.com/code/younusmohamed/49-02-sample-code)", "votes": null }, { "id": "3153916", "postDate": "03/19/2025 10:07:32", "content": "Yeah that is what I thought as well, I used your script yesterday and it worked perfectly! Thanks.
", "rawMarkdown": "Yeah that is what I thought as well, I used your script yesterday and it worked perfectly! Thanks.", "votes": null } ], "comments": [ { "id": 3132186, "author_name": "shiyili", "author_url": "", "post_date": "02/23/2025 20:36:03", "content": "Great thoughts and insights!
", "votes": null, "replies": [] }, { "id": 3136460, "author_name": "petershiyin", "author_url": "", "post_date": "02/28/2025 14:45:42", "content": "Great insights!
\nI am interested to integrate optical flow data, and need videos of optical flow (both dense and sparse) calculated from train and test dataset videos. Does anybody have those videos online?
", "votes": null, "replies": [] }, { "id": 3152990, "author_name": "elhnan", "author_url": "", "post_date": "03/18/2025 10:33:03", "content": "Great post! One question though, is it really possible to use videos with 1280×720 resolution? When I allocate the videos to my GPU it runs out of memory.
", "votes": null, "replies": [ { "id": 3153017, "author_name": "younusmohamed", "author_url": "", "post_date": "03/18/2025 11:06:38", "content": "You can try resizing the videos to something lesser. I have tried 224 X 224 in this notebook
", "votes": null, "replies": [ { "id": 3153916, "author_name": "elhnan", "author_url": "", "post_date": "03/19/2025 10:07:32", "content": "Yeah that is what I thought as well, I used your script yesterday and it worked perfectly! Thanks.
", "votes": null, "replies": [] } ] } ] } ], "raw_markdown_by_id": { "3132102": "Based on insights from previous video-based challenges (NFL Contact & Impact Detection, YouTube-8M, ASL Fingerspelling, and Isolated Sign Language Recognition) I have a discussion post on it that you can check out and the complete EDA performed (you can find my public notebook also), here are some ideas on how we can tackle the Crash Prediction competition:\n\n---\n\n**Data Preprocessing & EDA** \n- **CSV & Video Analysis:** \n - Leverage metadata (e.g., time-of-event, time-of-alert) to understand collision timing.\n - Extract key frames from dashcam videos, noting that most collisions occur mid-video (~40 sec).\n- **Normalization & Augmentation:** \n - Standardize frame resolution (1280×720) and adjust for varying brightness.\n - Apply temporal sampling (selecting frames before the collision event) and spatial cropping around vehicles.\n\n---\n\n**Model Architecture** \n- **3D CNN Approaches:** \n - Inspired by NFL solutions, use 3D CNNs (e.g., resnet50-irCSN) to capture spatio-temporal cues from video sequences.\n - Experiment with multi-view inputs if multiple dashcam angles are available.\n- **Transformer & Hybrid Models:** \n - Combine CNN-extracted features with Transformer layers to capture long-range temporal dependencies.\n - Consider hybrid architectures (e.g., CNN + RNN/Transformer) similar to approaches in YouTube-8M and ASL tasks.\n- **Optical Flow & Motion Features:** \n - Integrate optical flow data to better capture vehicle motion dynamics and predict imminent collisions.\n\n---\n\n**Augmentation & Regularization** \n- **Temporal Augmentations:** \n - Apply random time-warping, frame dropping, and shifting to simulate different collision timings.\n- **Spatial Augmentations:** \n - Use affine transformations (rotation, scaling, shifting) and brightness/contrast adjustments to improve robustness under varied weather and lighting.\n- **Regularization Techniques:** \n - Utilize heavy dropout, stochastic depth, and adversarial weight perturbation (AWP) to prevent overfitting.\n\n---\n\n**Postprocessing & Ensembling** \n- **Temporal Smoothing:** \n - Apply sliding-window filtering or temporal NMS to smooth predictions across adjacent frames.\n- **Ensembling:** \n - Blend multiple model outputs (e.g., 3D CNN, Transformer-based, hybrid models) using simple averaging or an XGBoost-based postprocessor to improve robustness.\n\n---\n\n**Inspiration from Previous Competitions** \n- **NFL Solutions:** \n - Multi-view processing and the use of tracking data can be adapted to fuse different dashcam perspectives and sensor inputs.\n- **YouTube-8M Strategies:** \n - Candidate generation followed by segment-level re-ranking can be adapted to localize early crash events.\n- **ASL Recognition Methods:** \n - Robust preprocessing (normalization, landmark extraction) and creative augmentations (CutMix, time masking) can inspire effective feature extraction from dashcam footage.\n\n---\n\n**Final Thoughts** \nFocusing on precise temporal localization, robust data augmentation, and hybrid spatio-temporal modeling is key. By integrating techniques from these past competitions and carefully designing our ensembling and postprocessing steps, we can create a powerful model for early crash prediction.\n\nHappy Kaggling – let’s push the boundaries in accident prediction!", "3132186": "Great thoughts and insights!", "3136460": "Great insights! \n\nI am interested to integrate optical flow data, and need videos of optical flow (both dense and sparse) calculated from train and test dataset videos. Does anybody have those videos online?", "3152990": "Great post! One question though, is it really possible to use videos with 1280×720 resolution? When I allocate the videos to my GPU it runs out of memory.", "3153017": "You can try resizing the videos to something lesser. I have tried 224 X 224 in this [notebook](https://www.kaggle.com/code/younusmohamed/49-02-sample-code)", "3153916": "Yeah that is what I thought as well, I used your script yesterday and it worked perfectly! Thanks." }, "source": "meta" }