| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | tags: |
| | - video-anomaly-detection |
| | - computer-vision |
| | - temporal-action-localization |
| | - ucf-crime |
| | - i3d-features |
| | datasets: |
| | - sultani/real-world-anomaly-detection-in-surveillance-videos |
| | task_categories: |
| | - video-classification |
| | size_categories: |
| | - 1GB<n |
| | pretty_name: UCF-Crime I3D Features |
| | pipeline_tag: video-classification |
| | widget: |
| | - example_title: Example Video Features |
| | example_code: | |
| | import h5py |
| | import numpy as np |
| | with h5py.File('ucf_crime_features_labeled.h5', 'r') as f: |
| | features = np.array(f['Abuse/Abuse001_x264/features'][:10]) |
| | labels = np.array(f['Abuse/Abuse001_x264/labels'][:10]) |
| | print("Features shape:", features.shape) |
| | print("Labels:", labels) |
| | subdir_list: |
| | - abuse |
| | - arrest |
| | - assault |
| | - arrest_features |
| | - arson_features |
| | - assault_features |
| | - burglary_features |
| | - explosion_features |
| | - fighting_features |
| | - normal_features |
| | - roadaccidents_features |
| | - robbery_features |
| | - shooting_features |
| | - shoplifting_features |
| | - stealing_features |
| | - vandalism_features |
| | |
| | data_files: |
| | - train: ucf_crime_features_labeled.h5 |
| | - validation: ucf_crime_features_labeled.h5 |
| | - test: ucf_crime_features_labeled.h5 |
| | --- |
| | |
| |
|
| |
|
| | # UCF-Crime: Precomputed I3D Features with Temporal Annotations |
| |
|
| | This dataset provides pre-extracted 1024-dimensional I3D RGB features along with frame-level temporal anomaly labels for videos from the UCF-Crime dataset. |
| |
|
| | --- |
| |
|
| | ## Dataset Characteristics |
| |
|
| | ### **Features** |
| | - 1024-dimensional I3D RGB feature vectors |
| | - Extracted from **64 uniformly sampled frames per video** |
| | - Feature tensor shape: **[64, 1024]** |
| |
|
| | ### **Temporal Annotations** |
| | - Mapped from original anomaly intervals |
| | - Re-scaled to match the 64 sampled frames |
| | - Only videos with valid annotations are included |
| |
|
| | ### **Coverage** |
| | - Videos that contain complete temporal anomaly intervals |
| | - Suitable for supervised learning tasks |
| |
|
| | --- |
| |
|
| | ## Recommended Usage |
| |
|
| | This dataset is ideal for: |
| |
|
| | - Frame-level binary classification |
| | - Reconstruction-based anomaly detection |
| | - Temporal convolutional networks (TCN) |
| | - Transformer-based sequence models |
| | - Sequential anomaly scoring models |
| |
|
| | Since features are already extracted, experiments are **lightweight and GPU-efficient**. |
| |
|
| | --- |
| |
|
| | ## Loading the Dataset |
| | The Data Loader code has also been provided. Please refer to that. |
| |
|
| |
|
| | --- |
| | ## Citation |
| | @inproceedings{sultani2018real, |
| | title={Real-world Anomaly Detection in Surveillance Videos}, |
| | author={Sultani, Waqas and Chen, Chen and Shah, Mubarak}, |
| | booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, |
| | pages={4469--4478}, |
| | year={2018} |
| | } |
| |
|
