Datasets:
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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 # Single file with splits via metadata
- 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}
}
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