Unsupervised Abnormality Detection Using Heterogenous Autonomous System

@article{Chowdhury2020UnsupervisedAD,
  title={Unsupervised Abnormality Detection Using Heterogenous Autonomous System},
  author={Sayeed Shafayet Chowdhury and Kazi Mejbaul Islam and Rouhan Noor},
  journal={2020 IEEE REGION 10 CONFERENCE (TENCON)},
  year={2020},
  pages={761-766}
}
Due to the rise of autonomous vehicles like drones and cars anomaly detection for better and robust surveillance becomes prominent for real-time recognition of normal and abnormal states. But the whole system fails if the unmanned device is unable to detect its own device's anomaly in real-time. Considering the scenario, we can make use of various data of autonomous vehicles like images, video streams, and other digital or analog sensor data to detect device anomaly. In this paper, we have… 

Figures and Tables from this paper

Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense

TLDR
An unsupervised ensemble anomaly detection system to detect device anomaly of an unmanned drone analyzing multimodal data like images and IMU sensor data synergistically and applied adversarial attack to test the robustness of the proposed approach and integrated defense mechanism.

Optimization of Surface Plasmon Resonance Biosensor for Analysis of Lipid Molecules

TLDR
This work uses finite-difference time-domain (FDTD) technique to perform quantitative analysis and finds that sensitivity increases when lipid concentration is increased and it is the highest for phospholipid and tryptophan combination when metal and lipid layer thicknesses are 45 nm and 30 nm respectively.

Anomaly detection using edge computing in video surveillance system: review

TLDR
This paper focuses on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance, and presents a systematic categorization of methodologies for anomaly detection.

References

SHOWING 1-10 OF 71 REFERENCES

Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems

TLDR
Self-supervised deep algorithms to detect anomalies in heterogeneous autonomous systems using frontal camera video and IMU readings and the composition of algorithms won runners up at the IEEE Signal Processing Cup anomaly detection challenge 2020.

Clustering Optimization for Abnormality Detection in Semi-Autonomous Systems

TLDR
An extension of Growing Neural Gas with the utility measurement is used for segmenting multisensory data into an optimal set of clusters that facilitate a semantic interpretation of data and define local linear models used for prediction purposes.

An Intelligent Video Surveillance System for Anomaly Detection in Home Environment Using a Depth Camera

TLDR
A simple yet efficient technique to detect fall with the help of inexpensive depth camera was presented and it was observed that SGD classifier gives better fall detection accuracy than the SVM classifier in both training and testing phase for SDU fall dataset.

Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

TLDR
This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.

Learning Probabilistic Awareness Models for Detecting Abnormalities in Vehicle Motions

TLDR
This paper shows how proposed filters enable the online identification of abnormal motions and decomposing the GP regression into spatial zones, where quasi-constant velocity models are valid, to build a set of Kalman filters that encode observed vehicle’s dynamics.

Anomaly Detection using Autoencoders in High Performance Computing Systems

TLDR
A novel approach for anomaly detection in HighPerformance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder, capable of detecting anomalies that have never been seen before with a very good accuracy.

Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes

Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks

TLDR
The findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach.

A survey of anomaly detection techniques in financial domain

Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving

TLDR
Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.
...