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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces.
Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing.
The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.
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Seitenzahl: 520
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing
1.1 Introduction
1.2 Background Subtraction
1.3 Mathematics Behind Background Subtraction
1.4 Gaussian Mixture Model
1.5 Principal Component Analysis
1.6 Applications
1.7 Conclusion
References
2 Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing
2.1 Introduction
2.2 Moving Object Detection
2.3 Envisaging the Object Detection
2.4 Conclusion
References
3 Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems
3.1 Introduction
3.2 Methods
3.3 Result
3.4 Conclusion
3.5 Limitations
3.6 Future Improvements
References
Further Reading
4 AI-Based Surveillance Systems for Effective Attendance Management: Challenges and Opportunities
4.1 Introduction
4.2 Artificial Intelligence (AI) and Smart Surveillance
4.3 Artificial Intelligence (AI) and Attendance Management
4.4 Technologies in Automatic Attendance Management Image Processing
4.5 Deep Learning and Various Neural Network Techniques for Attendance Management
4.6 Role of AI Technologies in Attendance Management
4.7 Challenges
4.8 Opportunities
4.9 Discussion & Conclusion
References
5 Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach
5.1 Introduction
5.2 History of Surveillance Systems
5.3 Literature Review
5.4 Mathematical Models for Surveillance Systems
5.5 Artificial Intelligence in Surveillance Systems
5.6 Use of Mathematical Models for Pre-Processing Image Data
5.7 Future Directions and Challenges
5.8 Conclusion
References
Key Terms
6 A Study on Object Detection Using Artificial Intelligence and Image Processing–Based Methods
6.1 Introduction
6.2 Role of Artificial Intelligence in Image Analysis
6.3 How Artificial Intelligence Can Enhance Traditional Image Processing Algorithms and Enable New Applications
6.4 Benefits of Artificial Intelligence and Image Processing Methods
6.5 Ethical Considerations Associated with AI and Image Processing
6.6 Conclusion
References
7 Application of Fuzzy Approximation Method in Pattern Recognition Using Deep Learning Neural Networks and Artificial Intelligence for Surveillance
7.1 Introduction
7.2 Preliminaries
7.3 Proposed Method
7.4 Experimental Analysis
7.5 Proposed Solution
7.6 Application Over Facial Recognition
7.7 Application of Thumb Impression Recognition
7.8 Advantages of the Proposed Method
7.9 Conclusion
References
8 A Deep Learning System for Deep Surveillance
8.1 Introduction
8.2 Related Work
8.3 Method and Approach
8.4 Model Implementations
8.5 Results and Comparative Analysis
8.6 Conclusions and Future Research Direction
References
9 Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection
9.1 Introduction
9.2 Literature Review
9.3 Approaches for MOD
9.4 Applications of AI and ML in MOD
9.5 Key Findings
9.6 Conclusion
References
10 Arduino-Based Robotic Arm for Farm Security in Rural Areas
10.1 Introduction
10.2 Literature Survey
10.3 Objectives of the Study
10.4 Significance of the Study
10.5 Working
10.6 Design of the Robotic Arm and Servo Motor Power
10.7 Fabrication
10.8 Results
10.9 Conclusion
References
11 Graph Neural Network and Imaging Based Vehicle Classification for Traffic Monitoring System
11.1 Introduction
11.2 Comprehensive Study of Vehicle Classification Technologies
11.3 Proposed Approach
11.4 Experiments and Results
11.5 Conclusion
References
12 A Novel Zone Segmentation (ZS) Method for Dynamic Obstacle Detection and Flawless Trajectory Navigation of Mobile Robot
12.1 Introduction
12.2 Related Work
12.3 Methodology
12.4 Evaluation
12.5 Conclusion
References
13 Artificial Intelligence in Indoor or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges and Applications
13.1 Introduction
13.2 Principles of AI-Powered Surveillance Systems
13.3 Machine Learning Algorithms
13.4 Benefits of Using AI in Surveillance Systems
13.5 Challenges of Using AI in Surveillance Systems
13.6 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Summarization of moving object detection using digital technology.
Chapter 7
Table 7.1 Category 1 capturing image in six frames.
Table 7.2 Category 2 capturing image in six frames.
Table 7.3 Category 3 capturing image in six frames.
Table 7.4 Category 4 capturing image in six frames.
Table 7.5 Category 5 capturing image in six frames.
Table 7.6 FAM analysis.
Chapter 8
Table 8.1 Model accuracies in different implementations.
Chapter 10
Table 10.1 List of components used and their specifications.
Table 10.2 Wavelength and distance range of IR sensors.
Table 10.3 Wavelength, distance and angle range of fire sensor.
Table 10.4 Operating speed of servo motors.
Chapter 11
Table 11.1 Comparison with competitive classifiers.
Table 11.2 Parameters of the GCN-R.
Chapter 12
Table 12.1 Comparative analysis of proposed technique with other techniques.
Table 12.2 Experimental comparison of various SLAM mediated strategic procedur...
List of Illustrations
Chapter 1
Figure 1.1 Background subtraction.
Figure 1.2 Flow chart of background subtraction algorithm.
Figure 1.3 General steps of background subtraction.
Figure 1.4 Mathematical concepts used background subtraction.
Chapter 2
Figure 2.1 Components and processes involved in the system of ANN.
Figure 2.2 Busy zone in highway [53].
Figure 2.3 Example of MOG2 background subtraction [53].
Figure 2.4 (Left) EOQ detection program. (Right) Close up video [53].
Chapter 3
Figure 3.1 Images from dataset.
Figure 3.2 Schematics of the ResNet101 architecture (Conv=convolutional; FC=Fu...
Figure 3.3 The residual node served as building block.
Figure 3.4 Depiction residual learning building block [10].
Figure 3.5 Updating rule for Adam.
Figure 3.6 Categorical cross-entropy loss function.
Figure 3.7 Accuracy vs Epoch.
Chapter 4
Figure 4.1 Generic flowchart of a face recognition algorithm.
Figure 4.2 General model of a Convolutional Neural Networks (CNN) (Source: Wi...
Figure 4.3 A simple recursive neural network architecture (Source: Wikimedia C...
Chapter 5
Figure 5.1 A broad division of surveillance.
Figure 5.2 AI-based surveillance integrates various actions, often in real tim...
Figure 5.3 Tasks and functions linked to surveillance mechanisms.
Figure 5.4 The different aspects of image processing.
Figure 5.5 Evolution of surveillance methods and latest technologies.
Chapter 6
Figure 6.1 The extraction of foreground (moving pixel) from a video sequence.
Figure 6.2 X-ray imaging of human upper body region.
Figure 6.3 MRI of human brain and extraction of tumor.
Figure 6.4 CT scan of human brain.
Chapter 7
Figure 7.1 Image processing.
Figure 7.2 Neural network and surveillance.
Figure 7.3 Neural network representation.
Figure 7.4 Representation of pattern recognition.
Figure 7.5 Self-organizing maps (Kohonen maps).
Figure 7.6 Neural network and surveillance.
Figure 7.7 Pascal’s triangle.
Figure 7.8 Face recognition processing flow.
Figure 7.9 Thumb impression processing flow.
Figure 7.10 Thumb impression processing flow.
Chapter 8
Figure 8.1 Applying filter and MaxPooling on an input feature map.
Figure 8.2 Applying sparse kernel and aggregation on coordinate map.
Figure 8.3 Generation of coordinate map.
Figure 8.4 Transpose convolution and pruning of coordinate map.
Figure 8.5 The process flow of the proposed system.
Figure 8.6 Image normalization for object detection.
Figure 8.7 Proposed CNN model layers.
Figure 8.8 The Spatially-sparse CNN architecture.
Figure 8.9 Training and testing accuracies of all implementations.
Figure 8.10 Object detection by the proposed system.
Chapter 10
Figure 10.1 Block diagram of farm security system.
Figure 10.2 CAD drawing of the robotic arm.
Figure 10.3 Flowchart of working of farm security system.
Figure 10.4 RR (2R) model of the robotic arm.
Figure 10.5 Kinematics of the model of robotic arm.
Figure 10.6 Geometric approach to determine distance of end effector.
Figure 10.7 Base of the robotic arm (All dimension in mm).
Figure 10.8 Primary arm (all dimensions in mm).
Figure 10.9 Secondary arm (all dimensions in mm).
Figure 10.10 3D printing process.
Figure 10.11 3D printed parts.
Figure 10.12 Detailed block diagram of components and connection circuit.
Chapter 11
Figure 11.1 Vehicle classification techniques.
Figure 11.2 Overview of the proposed approach.
Figure 11.3 Graph construction.
Figure 11.4 Bipartite graph formation.
Figure 11.5 Architecture of GCN-R.
Figure 11.6 Training and validation accuracy of the proposed GCN-R.
Figure 11.7 Training and validation loss of the proposed GCN-R.
Chapter 12
Figure 12.1 Overview of the proposed work with input, processor and desired ou...
Figure 12.2 Architecture of proposed methodology for obtaining manipulated tra...
Figure 12.3 (a) Geometrical representation of our customized mobile robot (CUB...
Figure 12.4 Positional changes of the mobile robot at instance with respect to...
Figure 12.5 Framewise point-to-point correlation and depth data obtained from ...
Figure 12.6 Path explored by the customized robot with loop closure integrated...
Figure 12.7 Zone segmentation for dynamic object prioritization.
Chapter 13
Figure 13.1 Sigmoid or logistic function [23].
Figure 13.2 SVM model [24].
Figure 13.3 Multiple hyperplanes separate the data from two classes [24].
Figure 13.4 Selecting hyperplane for data with outlier [25].
Figure 13.5 Hyperplane which is the most optimized one [25].
Figure 13.6 Original 1D dataset for classification [24].
Figure 13.7 Mapping 1D data to 2D to become able to separate the two classes [...
Figure 13.8 KNN algorithm working visualization [26].
Figure 13.9 The architecture of the region proposal network or RPN [31].
Figure 13.10 A hidden Markov model with three states and three observation sta...
Figure 13.11 Gaussian distribution visual representation [38].
Figure 13.12 Learning curves of training the model without compression.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Mathematical Models Using Artificial Intelligence for Surveillance Systems
Edited by
Padmesh Tripathi
Mritunjay Rai
Nitendra Kumar
and
Santosh Kumar
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394200580
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
Preface
A surveillance system consists of the collection, analysis, and dissemination of results for the purpose of prevention. This prevention may be related to healthcare, public security, business security, traffic monitoring, retail loss, crime, etc. For example, in healthcare, an effective disease diagnosis system is indispensable for detecting disease eruptions rapidly before they spread, threaten lives, and turn out to be difficult to control. The phenomenon of surveillance and surveillance activities using modern technologies are very important and are becoming increasingly in demand. Most organizations are using surveillance systems to protect them for different purposes. Even individuals are using surveillance systems to protect their houses and offices.
With the emergence of artificial intelligence, machine learning, and deep learning, surveillance systems have reached a standard. It made the surveillance system very powerful and effective in prevention. AI has become increasingly important in surveillance and security applications in recent years. AI is capable of processing and analyzing huge amounts of data in a fast and accurate manner, making it a treasured tool in detection and prevention. One of the most noteworthy benefits of AI-based surveillance systems is their capability to detect and track people and objects accurately. This tool can recognize faces, differentiate between objects and individuals, and identify unusual behavior. AI and ML algorithms can analyze video feeds in real-time, identifying patterns, anomalies, and key details that might go unnoticed by the human eye. They can enable advanced features like facial recognition, license plate recognition, and predictive analytics.
Mathematics plays a vital role in all areas of engineering and technology. Therefore, the mathematical models play a key role in the success of all the techniques. Different mathematical models have been used for different applications of AI.
The book “Mathematical Models Using Artificial Intelligence for Surveillance Systems” aims to provide readers with a wide-ranging understanding of various facets of mathematical models using AI in surveillance systems. This book is a compilation of case studies, research, and expert acumens that examine the challenges and opportunities that are faced in the surveillance system. It covers topics such as deep surveillance, farm security, moving object detection, attendance management, obstacle detection, remote control towers, traffic monitoring systems, etc. It provides an outstanding resource for understanding the mathematical models working behind different surveillance systems. The book chapters are written by specialists associated with engineering, mathematics, management, and traffic control systems.
Organization of the book
This book contains 13 chapters. A brief description of each of the chapters is as follows:
Chapter 1, titled “Elevating Surveillance Integrity-Mathematical Insights into background subtraction in Image Processing” presents a comprehensive exploration of background subtraction methods with a focus on elevating surveillance integrity through mathematical insights. A novel approach that integrates Gaussian Mixture Models (GMM) with adaptive learning rates has been proposed.
Chapter 2, titled “Machine Learning and Artificial Intelligence in the detection of moving objects using image processing,” presents the applications of digital technologies like the LBF algorithm, background subtraction algorithm, GMM (Gaussian Mixture Model), GANN (Generative adversarial neural networks), Kalman filter, fuzzy c-mean, end-of-queue, Delaunay triangulation, robust approaches, RPCA (Robust Principal Component Analysis), Semi-Automatic Vehicle Detection System (SAVDS), and 3D LiDAR (Light Detection and Ranging) in the deduction and tracking of objects.
Chapter 3, titled “Machine learning and imaging-based vehicle classification for traffic monitoring systems,” presents the applications of image analysis and machine learning algorithms to categorize cars for traffic control systems. In comparison to traditional approaches, this cutting-edge technology offers a more precise, effective, and versatile solution. As a result, our research has significant implications for enhancing traffic control and promoting public safety on the roads.
Chapter 4, titled “AI-Based Surveillance Systems for Effective Attendance Management: Challenges and Opportunities,” presents the application of deep learning in automatic attendance management (AAM) at various institutions.
Chapter 5, titled “Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach,” presents an extensive exploration of the integration of mathematical models and artificial intelligence (AI) techniques in surveillance systems based on image processing. The study delves into various mathematical modeling approaches and their fusion with AI techniques to address key challenges in object detection, recognition, behavior analysis, and video analytics within surveillance systems.
Chapter 6, titled “A Study on Object Detection Using Artificial Intelligence and Image Processing-Based Methods,” presents the integration of artificial intelligence and imaging and highlights key techniques and applications along the way. Deep learning has been used to detect the object.
Chapter 7, titled “Application of Fuzzy Approximation Methods in Pattern Recognition Using Deep Learning Neural Networks and Artificial Intelligence for Surveillance,” presents the idea of image processing techniques in surveillance for security systems. The main purpose of study is to build a constant monitoring system with the help of deep learning neural network and artificial intelligence by introducing a new fuzzification technique called Fuzzy Approximation Method (FAM).
Chapter 8, titled “A Deep Learning System for Deep Surveillance,” presents a deep learning model with different implementations of convolutional neural networks (CNNs) for deep surveillance applications. The proposed model leverages the power of SoftMax Regression, Support Vector Machine, Convolutional Neural Network, MatConvNet, and spatially-sparse CNN to achieve robust object detection, tracking, and anomaly detection from real-time video streams.
Chapter 9, titled “Study of Traditional, Artificial Intelligence, and Machine Learning Based Approaches for Moving Object Detection,” presents a comprehensive overview of the state-of-the-art techniques and algorithms that leverage traditional, AI, ML, and DL for moving object detection (MOD).
Chapter 10, titled “Arduino-Based Robotic Arm for Farm Security in Rural Areas,” presents different technologies used for farming and providing security to farm land, particularly IoT, ICT, and GPS-based, followed by the design of a robotic manipulator or arm with a camera to watch over the farm. The system is controlled by the Arduino platform, which receives and communicates with and from the user’s mobile application through wireless controlling signals.
Chapter 11, titled “Graph Neural Networks and Imaging Based Vehicle Classification for Traffic Monitoring System,” presents the potential of sophisticated graph-based learning paradigms in reshaping understanding and optimization of traffic dynamics, paving the way for smarter and more effective urban mobility solutions.
Chapter 12, titled “A novel Zone Segmentation (ZS) method for dynamic obstacle detection and flawless trajectory navigation of mobile robot,” presents the incorporation of various LiDAR data in correlation with the Intel RealSense camera’s depth data for optimized visibility graph formation (CGO) as well as enhancement of map planning (MP). The working schematic and visual results are structured in a way to be considered as analytical evidence of the proposed architecture.
Chapter 13, titled “Artificial Intelligence in Indoor or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges and Applications,” presents a comprehensive and systematic exploration of the role of AI in surveillance, offering a detailed overview of its principles, challenges, and wide-ranging applications.
Padmesh Tripathi
Delhi Technical Campus, Greater Noida, (U.P.), India
Mritunjay Rai
Shri Ramswaroop Memorial University, Barabanki, (U.P.), India
Nitendra Kumar
Amity Business School, Amity University, Noida, (U.P.), India
Santosh Kumar
Department of Mathematics, Sharda University, Greater Noida, (U.P.), India
2Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing
K. Janagi1*, Devarajan Balaji2, P. Renuka1 and S. Bhuvaneswari3
1Department of Mathematics, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
2Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
3Department of Physics, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Abstract
Digital technology plays a major role in various fields like real life, science and engineering. This chapter deals with the uses of digital technology in detecting and tracking of objects. In particular, it applies LBF algorithm, background subtraction algorithm, GMM model (Gaussian Mixture Model), GANN (Generative adversarial neural networks), Kalman filter, Fuzzy c-mean, End-of-Queue (EOQ), Delaunay triangulation, robust approaches, RPCA (Robust Principal Component Analysis), Semi-Automatic Vehicle Detection System (SAVDS), and 3D LiDAR (Light Detection and Ranging). Based on the above-mentioned algorithms one can easily detect the moving object or track the object (in most of the cases a human being) more accurately. Generally, all the algorithms including RPCA, NLTFN (Non-Convex Logarithm Fraction Norms), RNLTFN (Robust Non-Convex Logarithm Fraction Norms) are mostly used to segregate the human images from other images, detection of images in poor weather conditions, monitoring the traffic in the vibrant work places and so on. These algorithms can be combined to provide the solutions as human intelligence. It can be envisaged to predict the technology based on the trend being observed by literatures.
Keywords: Moving objects detection, Robust approach, GMM model, NLTFN technique
2.1 Introduction
There are several strategies, such as background subtraction, statistical approaches, and temporal frame differencing, for finding moving objects. The various tracking techniques, including Point tracking, Silhouette tracking, and Kernel tracking, were also discussed in [1]. A practical approach to fixing Gaussian Mixture Model (GMM) concerns is by speeding up the process of updating specific pixels. To do this, a hidden observer was put into place. The extracted image was segmented into squares of uniform size, and spotters were placed in each. These observers evaluated the histograms of their areas to detect unusual fluctuations in the natural world. Spotters’ reports were used to update a static representation of the world. The algorithm is tested on four different video databases covering a wide range of conditions in order to gauge how well it will perform in the real world. The outcomes of these experiments proved the viability of this method [2]. The use of appropriate algorithms, such as Zernike moments and support vector machine (SVM), has been attempted to accomplish accurate detection of moving targets. The method’s goal is to locate the targets’ invariant characteristics across video frames. Zernike moments, which are robust to transformations, are used to extract features in the proposed algorithm. They use multiplexes SVMs, which are an ensemble of classifiers, to improve efficiency and lower tracking errors across a series of photos. Fuzzy logic is often used to recognize items in moving pictures [3].
Object tracking’s primary and most important function is to extract a Region of Interest (ROI) from a video scene. When an item is in view, its position, velocity, and occlusion are all tracked in real time. Detecting and categorizing objects in the provided video is the first step before tracking can begin. The existence and precise locations of objects in a video are ascertained by object detection. Humans, pedestrians, automobiles, and other moving objects are among those recognized and afterwards sorted into their respective groups. Object tracking is achieved by monitoring the existence, size, and position of objects over the course of a video series to detect any changes. Object tracking is useful in many fields, such as security cameras, AI, traffic cameras, and animated shorts. There is a breakdown and evaluation of several detecting methods [4]. A revised approach for extracting movable objects is using a codebook. This codebook algorithm is based on a perceptual approach, hence optimizing the complexity with which foreground information is extracted. The primary goal of this adaptive technique is to lessen the algorithmic burden of foreground detection without sacrificing precision. In order to capture the spatial connections between pixels, this technique employs a super pixel segmentation strategy. Super pixels near the possible foreground object locations are prioritized during processing. This improves the algorithm’s performance in foreground detection. The suggested algorithm is verified using a freely accessible data set of sequences with moving backgrounds and compared to other state-of-the-art algorithms. During foreground identification, the proposed approach obtains the greatest frame processing rate, as shown by experimental findings [5]. Hu et al. [6] presented a machine learning and clustering-based framework for motion detection that can adjust to new environments. The model is first trained with a set of test photos, with a focus on correct scene-specific parameters. When motion is detected, any notable changes in the same region are grouped together into “change clusters.” The model makes use of clustering methods to produce an average-linkage clustering class minimum spanning tree (MST). To spot shifts in the pictures, we compare them to the average shortest distance of the MST. The scene can then be efficiently monitored using a combination of the training settings and the detection algorithm.
The model’s detection accuracy is enhanced by using clustering to enhance quality factors during mock practice session. The experiments validate the model’s high degree of flexibility and accuracy in motion detection. Concern for people’s safety and security has increased during the past few years. As a result of this heightened worry, there has been a dramatic increase in the worldwide installation of surveillance cameras. As a result, researchers have focused more on developing better automated surveillance systems. At the same time, there has been a boom in biometrics study due to the pressing need to find solutions to the problems that emerge in such settings. Despite these advances, there is still not a foolproof automated surveillance system that can recognize biometrics in such settings. However, with the development of new biometric and motion analysis technologies, a fully automated system may soon be within reach. The authors draw attention to two key differences between the reviewed research and its predecessors: first, it places an emphasis on methods optimized for use in open spaces and surveillance settings. Second, unlike anomaly detection, or action recognition, behavior analysis, biometric recognition is the ultimate goal of the surveillance system [7
