Normal Vs. Robbery

This dataset is a refined subset of the UCF Crime Dataset, specifically focusing on Robbery and Normal activity classes. Each class contains 150 high-quality preprocessed videos, making it an ideal dataset for machine learning, deep learning, and computer vision applications such as anomaly detection and human activity recognition.

Key Features of the Dataset

Preprocessed & Standardized – Each video is resized to 600 frames (~20 seconds) while retaining complete contextual information.
Balanced Dataset – Includes 150 Robbery videos and 150 Normal videos, ensuring equal representation.
Optimized for AI & ML Models – Ready-to-use for TensorFlow, PyTorch, OpenCV, and deep learning frameworks.
Simplifies Anomaly Detection – The original UCF Crime videos were long and untrimmed, making them difficult to work with. This dataset eliminates that complexity.
Efficient Sampling Techniques – Uses linear and random sampling to preserve essential details while reducing video length.
Real-World Applications – Ideal for security surveillance, human behavior analysis, and crime detection systems.
Compatible with Various AI Tasks – Supports classification, prediction, and real-time monitoring systems.

 Applications of This Dataset

Abnormal Human Behavior Recognition & Prediction
Robbery Recognition & Prevention Systems
Anomaly Detection in Surveillance Videos
Real-Time Crime Monitoring & AI-driven Security
Deep Learning Research in Computer Vision

This dataset is sourced from Kaggle.