Datasets:

Rahima411
/

ucf-anomaly-detection-mapped

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} }