Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos
This work addresses anomaly detection in surveillance videos, offering a potential solution to problems like noise and concept drift, though it appears incremental as it adapts an existing HTM algorithm to a specific domain.
The paper tackles video anomaly detection by proposing Grid HTM, a novel Hierarchical Temporal Memory-based architecture designed for complex videos like surveillance footage, which leverages HTM's noise tolerance and online learning to address issues in deep learning methods.
The interest for video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning to perform anomaly detection in videos, but this approach has multiple problems. For starters, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknowness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature, but even they suffer from general deep learning issues and are hard to train properly. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, namely, Grid HTM, which is an HTM-based architecture specifically for anomaly detection in complex videos such as surveillance footage.