Streaming Data-Based Video Anomaly Detection Method with Adaptive Feature Selection

Video anomaly detection in surveillance scenarios can be a challenging task, but a new method is shaking things up in the world of deep learning and batch processing. This cutting-edge approach leverages data streams to analyze video segments incrementally, adapting to the continuous and evolving nature of surveillance footage.

One key innovation is the use of Discrete Cosine Transform (DCT) to extract temporal features from the video segments, which are then processed using Accelerated Particle Swarm Optimization (APSO) for efficient feature selection. This not only reduces dimensionality but also retains essential information, all while improving computational efficiency.

The magic doesn’t stop there. The selected features are fed into a powerful ensemble of Unsupervised Feature Representative Online Sequential Extreme Learning Machines (UFROS-ELMs), which are designed to adapt to changing data distributions in real-time. This adaptation is crucial for handling the dynamic nature of real-world surveillance data, making sure no anomaly goes unnoticed.

But does it work? You bet. Experimental results on benchmark datasets like UCF-Crime and UCSD Pedestrian show that this method outperforms existing approaches, delivering higher accuracy, lower error rates, and computational efficiency suitable for real-time applications. By combining DCT/APSO feature extraction with adaptive UFROS-ELM learning, this streaming data-based approach is paving the way for cost-effective and precise anomaly detection in surveillance videos.