Fine-Tuning a Pre-trained CAE for Deep One Class Anomaly Detection in Video Footage

2021 ◽  
pp. 3-17
Author(s):  
Slim Hamdi ◽  
Hichem Snoussi ◽  
Mohamed Abid
Keyword(s):  
Anomaly Detection ◽  
Fine Tuning ◽  
Video Footage

scholarly journals Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3993
Author(s):  
Mohammad Ibrahim Sarker ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
David Fuentes-Jiménez ◽  
Sara Luengo-Sánchez
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Learning Algorithm ◽  
Qualitative Evaluation ◽  
High Sensitivity ◽  
Fine Tuning ◽  
Surveillance Video ◽  
Emergency Situations ◽  
In The Wild ◽  
Spatio Temporal

Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them.

scholarly journals A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 135 ◽  
Author(s):  
Xu Kang ◽  
Bin Song ◽  
Fengyao Sun
Keyword(s):  
Anomaly Detection ◽  
Video Data ◽  
Fine Tuning ◽  
Generative Adversarial Network ◽  
Similarity Metric ◽  
Attention Model ◽  
Real Time Analysis ◽  
Percentage Points ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method.

scholarly journals Automatic Detection of Violent Incidents from Video Footage of CCTV Cameras

2020 ◽  
pp. 464-472
Author(s):  
Baswaraju Swathi ◽  
B L Deepika Chowdary ◽  
K Sai Sindhu ◽  
Ashika P
Keyword(s):  
Anomaly Detection ◽  
State Of The Art ◽  
Traditional Approach ◽  
University Campus ◽  
Major Drawback ◽  
Public Places ◽  
Human Errors ◽  
Video Footage ◽  
Public Garden ◽  
Detection And Recognition

In the current era, the majority of public places such as supermarket, public garden, malls, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 ? 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. We present an e?cient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.

scholarly journals MADICS: A Methodology for Anomaly Detection in Industrial Control Systems

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1583
Author(s):  
Ángel Luis Perales Gómez ◽  
Lorenzo Fernández Maimó ◽  
Alberto Huertas Celdrán ◽  
Félix J. García Clemente
Keyword(s):  
Deep Learning ◽  
Water Treatment ◽  
Anomaly Detection ◽  
Control Systems ◽  
Learning Algorithms ◽  
Treatment Plant ◽  
Cyber Attacks ◽  
Fine Tuning ◽  
Industrial Control ◽  
Industrial Control Systems

Industrial Control Systems (ICSs) are widely used in critical infrastructures to support the essential services of society. Therefore, their protection against terrorist activities, natural disasters, and cyber threats is critical. Diverse cyber attack detection systems have been proposed over the years, in which each proposal has applied different steps and methods. However, there is a significant gap in the literature regarding methodologies to detect cyber attacks in ICS scenarios. The lack of such methodologies prevents researchers from being able to accurately compare proposals and results. In this work, we present a Methodology for Anomaly Detection in Industrial Control Systems (MADICS) to detect cyber attacks in ICS scenarios, which is intended to provide a guideline for future works in the field. MADICS is based on a semi-supervised anomaly detection paradigm and makes use of deep learning algorithms to model ICS behaviors. It consists of five main steps, focused on pre-processing the dataset to be used with the machine learning and deep learning algorithms; performing feature filtering to remove those features that do not meet the requirements; feature extraction processes to obtain higher order features; selecting, fine-tuning, and training the most appropriate model; and validating the model performance. In order to validate MADICS, we used the popular Secure Water Treatment (SWaT) dataset, which was collected from a fully operational water treatment plant. The experiments demonstrate that, using MADICS, we can achieve a state-of-the-art precision of 0.984 (as well as a recall of 0.750 and F1-score of 0.851), which is above the average of other works, proving that the proposed methodology is suitable for use in real ICS scenarios.

Fine-Tuning Futures

ASHA Leader ◽  
2017 ◽  
Vol 22 (6) ◽  
Author(s):  
Christi Miller
Keyword(s):  
Fine Tuning

Food Safety Security: A new Concept for Enhancing Food Safety Measures

2012 ◽  
Vol 82 (3) ◽  
pp. 216-222 ◽  
Author(s):  
Venkatesh Iyengar ◽  
Ibrahim Elmadfa
Keyword(s):  
Food Safety ◽  
Hazard Analysis ◽  
Warning System ◽  
Shared Responsibility ◽  
Fine Tuning ◽  
Strategic Action ◽  
Surveillance Network ◽  
Measurement Systems ◽  
Critical Control Points ◽  
The Impact

The food safety security (FSS) concept is perceived as an early warning system for minimizing food safety (FS) breaches, and it functions in conjunction with existing FS measures. Essentially, the function of FS and FSS measures can be visualized in two parts: (i) the FS preventive measures as actions taken at the stem level, and (ii) the FSS interventions as actions taken at the root level, to enhance the impact of the implemented safety steps. In practice, along with FS, FSS also draws its support from (i) legislative directives and regulatory measures for enforcing verifiable, timely, and effective compliance; (ii) measurement systems in place for sustained quality assurance; and (iii) shared responsibility to ensure cohesion among all the stakeholders namely, policy makers, regulators, food producers, processors and distributors, and consumers. However, the functional framework of FSS differs from that of FS by way of: (i) retooling the vulnerable segments of the preventive features of existing FS measures; (ii) fine-tuning response systems to efficiently preempt the FS breaches; (iii) building a long-term nutrient and toxicant surveillance network based on validated measurement systems functioning in real time; (iv) focusing on crisp, clear, and correct communication that resonates among all the stakeholders; and (v) developing inter-disciplinary human resources to meet ever-increasing FS challenges. Important determinants of FSS include: (i) strengthening international dialogue for refining regulatory reforms and addressing emerging risks; (ii) developing innovative and strategic action points for intervention {in addition to Hazard Analysis and Critical Control Points (HACCP) procedures]; and (iii) introducing additional science-based tools such as metrology-based measurement systems.

Fine-Tuning Sought for Monitoring Celiac Disease Patients

2008 ◽  
Vol 38 (11) ◽  
pp. 30
Author(s):  
KATE JOHNSON
Keyword(s):  
Celiac Disease ◽  
Fine Tuning

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Heritage

2018 ◽  
Author(s):  
Omar Shaikh ◽  
Stefano Bonino
Keyword(s):  
Facial Expressions ◽  
South Asian ◽  
Online Video ◽  
The South ◽  
Men And Women ◽  
The Public ◽  
Video Footage ◽  
Community Heritage ◽  
Personal Accounts ◽  
The Way

The Colourful Heritage Project (CHP) is the first community heritage focused charitable initiative in Scotland aiming to preserve and to celebrate the contributions of early South Asian and Muslim migrants to Scotland. It has successfully collated a considerable number of oral stories to create an online video archive, providing first-hand accounts of the personal journeys and emotions of the arrival of the earliest generation of these migrants in Scotland and highlighting the inspiring lessons that can be learnt from them. The CHP’s aims are first to capture these stories, second to celebrate the community’s achievements, and third to inspire present and future South Asian, Muslim and Scottish generations. It is a community-led charitable project that has been actively documenting a collection of inspirational stories and personal accounts, uniquely told by the protagonists themselves, describing at first hand their stories and adventures. These range all the way from the time of partition itself to resettling in Pakistan, and then to their final accounts of arriving in Scotland. The video footage enables the public to see their facial expressions, feel their emotions and hear their voices, creating poignant memories of these great men and women, and helping to gain a better understanding of the South Asian and Muslim community’s earliest days in Scotland.

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