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

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

Of the matrics of the equilibrium of the rod formation system based on the principle the duality of the problems of structures mechanics

The formation of the equilibrium matrix of the core system in the matrix form is based on the use of the mechanical model of the system obtained by its discretization. The topological structure of the model is set using the graph and the accompanying incidence matrix. The matrix transformation of the vector of nodal displacements in combination with the extended incidence matrix allows determining the absolute elongations and distortions of each finite element. The composition of only two matrices (matrix of incidence and lengths of elements) and the skew vector leads to a geometric matrix characterizing the dependence of concentrated bending deformations in the calculated cross sections of the core system from the nodal displacements for a given load. Based on the duality principle, by transposing the geometric matrix, the equilibrium equation of the core system is derived in matrix form

Hierarchical Temporal Memory for Anomaly Detection in Videos

The use of video anomaly detection systems has gained traction for the past few years. The current approaches use deep learning for performing anomaly detection in videos, but this has multiple problems. For starters, deep learning in general has issues with noise, concept drift, explainability, and training data volume. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknowness, heterogeneity, and class imbalance. Anomaly detection in 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. This thesis instead looks to Hierarchical Temporal Memory (HTM) to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. This thesis introduces Grid HTM, which is a HTM-based architecture specifically for anomaly detection in complex videos such as surveillance footage. Experiment results show that, with proper data and further refinements Grid HTM can be used for anomaly detection in complex videos

Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in Videos

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.Comment: 7 page

Decade Dynamics of Sable Skull Size in the Baikal Region

Для пяти популяций соболя Прибайкалья установлены долговременные (за вторую половину XX века) разнонаправленные изменения размеров черепа. В разных популяциях отмечены как тренды увеличения (0,5–1,5 %), так и уменьшения (1,26 до 2,47 %) кондилобазальной длины черепа. Различия в междекадных отклонениях размеров не значимы статистически и носят разнонаправленный характер, не нарушая популяционную стабильность. В большинстве случаев динамика изменений размеров у самцов и самок когерентна, однако сильнее выражена у самцов. Оценен размерный статус изученных группировок – все они относятся к мелким формам видаFor the five populations of Baikal sable evaluated long-term (up to 50 years) changes in the skull size. In different populations trends the increasing condylobasal skull length by 0,5-1,5 %, and reducing from 1,26 to 2,47 % are observed. Differences of decadal deviations in skull size are multidirectional, not significant statistically, and do not violate population morphological stability. In most cases, the dynamics of sizes between the sexes is coherent, but more expressed in males. We estimated the size status of the studied groups – all of them belong to small forms of the specie

Decade Dynamics of Sable Skull Size in the Baikal Region

Для пяти популяций соболя Прибайкалья установлены долговременные (за вторую половину XX века) разнонаправленные изменения размеров черепа. В разных популяциях отмечены как тренды увеличения (0,5–1,5 %), так и уменьшения (1,26 до 2,47 %) кондилобазальной длины черепа. Различия в междекадных отклонениях размеров не значимы статистически и носят разнонаправленный характер, не нарушая популяционную стабильность. В большинстве случаев динамика изменений размеров у самцов и самок когерентна, однако сильнее выражена у самцов. Оценен размерный статус изученных группировок – все они относятся к мелким формам видаFor the five populations of Baikal sable evaluated long-term (up to 50 years) changes in the skull size. In different populations trends the increasing condylobasal skull length by 0,5-1,5 %, and reducing from 1,26 to 2,47 % are observed. Differences of decadal deviations in skull size are multidirectional, not significant statistically, and do not violate population morphological stability. In most cases, the dynamics of sizes between the sexes is coherent, but more expressed in males. We estimated the size status of the studied groups – all of them belong to small forms of the specie

Genetic individualization of sable (Martes zibellina L. 1758) using microsatellites

Genetic individualization based on non-invasive sampling is crucial for estimating the numbers of individuals in endangered mammalian populations. In sable (Martes zibellina)-poaching cases, identifying the number of animals involved is critical for determining the penalty. In addition, investigating animal numbers for wild sable populations requires genetic individualization when collecting several samples in neighboring regions. Microsatellites have been demonstrated to be reliable markers for individual identification. Thirty-three microsatellite loci derived from Mustelidae were selected to develop a genetic individualization method for sable. Three reference populations containing 54 unrelated sables were used to calculate allele number, allelic frequencies, and the polymorphic information content of each locus. The data were subsequently used to assess the validity of a combination of twelve loci for sable individualization. We defined twelve polymorphic loci that were easy to be amplified and genotyped. Four significant deviations from Hardy-Weinberg equilibrium were observed among the 12 loci in the three populations. The match probability of an individual from the reference populations with a random individual based on the 12 loci was 1.37 × 10−13. Using the combination of the twelve loci provides sufficient power to individualize sables considering the levels of microsatellite polymorphism observed. These loci were successfully applied to a case of sable poaching and provided valid evidence to determine the penalty. The genetic individualization of sable based on these loci might also be useful to investigate the numbers of animals in wild populations