GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos

Abstract

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, 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 unknownness, 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; however, even they suffer from general deep learning issues and are hard to properly train. 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, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos.

IoT and Deep Learning-Based Farmer Safety System

“…Several HTM use cases in the research studies [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ] and practices that have already been commercially tested include server anomaly detection using Grok [ 70 ], stock volume anomalies [ 71 ], rogue human behavior [ 72 ], natural language prediction [ 73 ], and geospatial tracking [ 74 ].…”

Online Video Anomaly Detection

“…The incremental spatio-temporal learner in [44] updated and distinguished new abnormal and normal behaviors in real time through active learning with fuzzy aggregation for anomaly detection and localization in real-time surveillance video streams. In addition, Monakhov et al [45] proposed an algorithm for anomaly detection in complex videos such as surveillance video. The algorithm is based on the HTM architecture of a grid, which simplifies data by segmentation technology and realizes real-time unsupervised anomaly detection.…”