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The document proposes a smart city system using facial recognition on surveillance cameras to track suspects in real-time. It outlines the current manual process police use to search surveillance footage which is time-consuming. The proposed system would use machine learning to create a facial recognition model trained on government ID photos. This would allow the system to automatically detect and alert police to a suspect's location across multiple cameras in real-time, improving efficiency over the current manual process. Concerns about privacy and costs are mentioned as challenges that would need to be addressed for such a system.
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Home work
This document proposes a smart city system using facial recognition on surveillance cameras to track criminal suspects in real-time. It outlines the current manual process police use to search surveillance footage which is time-consuming. The proposed system would use machine learning to create a facial recognition model trained on a government database. This would allow the system to automatically detect and track suspects on cameras, informing police instantly of their location. Benefits mentioned include increased safety, reduced crime rates, and time savings for both the public and law enforcement.
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This document describes the development of an intelligent video surveillance system using OpenCV. The proposed system aims to reduce electricity usage and storage needs by only recording video when human presence is detected, as opposed to continuous recording. It utilizes a camera initialized through OpenCV to capture video frames. The frames are converted to grayscale and analyzed using a Haar cascade classifier to detect human faces. If a face is detected, recording begins. If no motion is detected for several seconds, recording will stop. The recorded videos are stored locally. This approach is well-suited for locations with intermittent human presence, where continuous recording is unnecessary. It allows for more efficient use of resources than traditional CCTV.
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This document proposes using deep learning approaches like convolutional neural networks to detect suspicious activities from surveillance videos. It aims to overcome limitations of existing depth sensors by using neural networks for more accurate human pose estimation. The system would monitor public areas through video surveillance and categorize human activities as usual or suspicious in real-time. It addresses gaps in previous research which focused on images rather than videos. The proposed methodology includes collecting and preprocessing surveillance video data, designing and training deep learning models to recognize and classify activities, and integrating the system with existing surveillance infrastructure for deployment.
Smart surveillance
This document discusses smart surveillance technologies, including their applications, system architectures, key technologies, challenges, and implications. It describes three main applications of smart surveillance: (1) generating real-time alerts of predefined or unusual activities, (2) enabling automatic forensic video retrieval through content-based indexing of video, and (3) enhancing situation awareness through joint tracking of objects' identities, locations, and activities over multiple cameras. It also outlines three common smart surveillance system architectures and several sections that will discuss the technologies, challenges, and implications in more detail.
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This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
ATM Security System Based on the Video Surveillance Using Neural Networks
This document proposes an ATM security system based on video surveillance using neural networks. The system uses computer vision and deep learning techniques to monitor ATM activity and detect unusual events like multiple people entering, cameras being removed, or people with obscured faces. Video frames are analyzed to identify objects and detect abnormal behaviors. If anything unusual is detected, an alert will be sent immediately to the nearest station and the ATM doors will lock. The system is intended to provide real-time security monitoring at low cost as an improvement over relying solely on human security guards. It describes the methodology used, including video acquisition, frame conversion, preprocessing, background modeling, feature extraction using algorithms like convolutional neural networks to identify faces and detect abnormal situations.
A Review on Motion Detection Techniques
Motion detection is the process of detecting moving objects in background images. Motion detection plays a fundamental role in any object tracking or video surveillance algorithm. The reliability with which potential foreground objects in movement can be identified, directly impacts on the efficiency and performance level achievable by subsequent processing stages of tracking or object recognition. The system automatically performs a task and gives alert to security in an area. This paper represents review on Motion detection is an essential for many video applications such as video surveillance, military reconnaissance, and robotics. Most of these applications demand low power consumption, compact and lightweight design, and high speed computation platform for processing image data in real time. Miss. Aditi Kumbhar | Dr. Pradip Bhaskar"A Review on Motion Detection Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5928.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/5928/a-review-on-motion-detection-techniques/miss-aditi-kumbhar
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This document describes a proposed intruder detection system using a camera. The system would work as follows:
1. A camera would continuously capture video frames and an image processing processor would compare the latest frame to a static threshold frame to detect differences.
2. If a difference is detected above a certain threshold using a sum of absolute differences (SAD) algorithm, it would indicate a potential intruder.
3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
The goal is to create an affordable intruder detection system that can detect intruders and raise alerts, providing security for places when unattended.
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This document proposes a smart city system using facial recognition on surveillance cameras to track criminal suspects in real-time. It outlines the current manual process police use to search surveillance footage which is time-consuming. The proposed system would use machine learning to create a facial recognition model trained on a government database. This would allow the system to automatically detect and track suspects on cameras, informing police instantly of their location. Benefits mentioned include increased safety, reduced crime rates, and time savings for both the public and law enforcement.
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DEEP LEARNING APPROACH FOR SUSPICIOUS ACTIVITY DETECTION FROM SURVEILLANCE VIDEO
This document proposes using deep learning approaches like convolutional neural networks to detect suspicious activities from surveillance videos. It aims to overcome limitations of existing depth sensors by using neural networks for more accurate human pose estimation. The system would monitor public areas through video surveillance and categorize human activities as usual or suspicious in real-time. It addresses gaps in previous research which focused on images rather than videos. The proposed methodology includes collecting and preprocessing surveillance video data, designing and training deep learning models to recognize and classify activities, and integrating the system with existing surveillance infrastructure for deployment.
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This document discusses a proposed real-time video surveillance system that utilizes machine learning, computer vision, and image processing algorithms. The system aims to detect and analyze objects of interest in CCTV footage in order to identify suspicious activities and assist authorities. It employs algorithms for face detection and recognition, as well as detection of weapons and abnormal movements. The system uses frameworks like OpenCV and TensorFlow to perform tasks like facial analysis, age and gender estimation, human pose estimation, and weapon detection in real-time video streams. It analyzes existing algorithms and evaluates their suitability for the system. The results of implementing and testing various algorithms on sample footage are also presented.
ATM Security System Based on the Video Surveillance Using Neural Networks
This document proposes an ATM security system based on video surveillance using neural networks. The system uses computer vision and deep learning techniques to monitor ATM activity and detect unusual events like multiple people entering, cameras being removed, or people with obscured faces. Video frames are analyzed to identify objects and detect abnormal behaviors. If anything unusual is detected, an alert will be sent immediately to the nearest station and the ATM doors will lock. The system is intended to provide real-time security monitoring at low cost as an improvement over relying solely on human security guards. It describes the methodology used, including video acquisition, frame conversion, preprocessing, background modeling, feature extraction using algorithms like convolutional neural networks to identify faces and detect abnormal situations.
A Review on Motion Detection Techniques
Motion detection is the process of detecting moving objects in background images. Motion detection plays a fundamental role in any object tracking or video surveillance algorithm. The reliability with which potential foreground objects in movement can be identified, directly impacts on the efficiency and performance level achievable by subsequent processing stages of tracking or object recognition. The system automatically performs a task and gives alert to security in an area. This paper represents review on Motion detection is an essential for many video applications such as video surveillance, military reconnaissance, and robotics. Most of these applications demand low power consumption, compact and lightweight design, and high speed computation platform for processing image data in real time. Miss. Aditi Kumbhar | Dr. Pradip Bhaskar"A Review on Motion Detection Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd5928.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/5928/a-review-on-motion-detection-techniques/miss-aditi-kumbhar
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This document describes a proposed intruder detection system using a camera. The system would work as follows:
1. A camera would continuously capture video frames and an image processing processor would compare the latest frame to a static threshold frame to detect differences.
2. If a difference is detected above a certain threshold using a sum of absolute differences (SAD) algorithm, it would indicate a potential intruder.
3. The system would then raise an alert if an intruder is confirmed, such as sending an email or message with the captured photo of the intruder.
The goal is to create an affordable intruder detection system that can detect intruders and raise alerts, providing security for places when unattended.
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The document describes a proposed system for detecting suspicious events using background subtraction for surveillance systems. The system first obtains foreground objects using background subtraction. The foreground objects are then classified as people or suspicious objects and tracked over time using blob matching. By analyzing the temporal and spatial properties of the tracked blobs, activities are classified as normal or suspicious, such as theft of objects. The system aims to more efficiently detect suspicious human behavior and objects for applications such as security and surveillance.
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Supervised Machine Learning Intelligence platform and knowledge Management for improved Situational Awareness
Real Time Detection of Moving Object Based on Fpga
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
F011113741
1. The document describes a method for real-time detection of moving objects based on background subtraction and its implementation on an FPGA. A static camera is used to capture video frames. The first frame is used as the reference background frame. Pixels in subsequent frames are compared to the background frame and objects are detected where pixel differences exceed a threshold.
2. The method was tested on sample surveillance videos. Background subtraction accurately detected moving objects in test videos in real-time. Future work may include identifying objects using face or palm recognition and activity recognition for visual surveillance applications.
SMART SURVEILLANCE SYSTEM USING LBPH ALGORITHM
The document describes a proposed smart surveillance system using the LBPH algorithm and structural similarity index measure (SSIM). The system uses LBPH for face recognition as an authentication procedure when an unknown face is detected. It also uses SSIM to detect differences between frames and identify objects that have been removed from the surveillance area. The system aims to provide intelligent CCTV that can not only record video but also process video in real-time to detect unwanted persons entering the surveillance area. It discusses the algorithms used, including LBPH, SSIM, and a literature review of related work on smart surveillance systems.
Developing technology
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The document proposes a facial recognition system using CCTV video to identify individuals and generate timestamp data on their presence. It involves three steps: 1) face detection on video frames, 2) super resolution to standardize face sizes, and 3) face recognition using a Siamese network to identify known and new identities with one-shot learning. The system aims to reduce time spent reviewing surveillance footage for law enforcement. It analyzes existing research on low-resolution face recognition, pedestrian detection, and proposes its pipeline as a solution to semi-automate target individual tracking from video data through facial matching and timestamps.
Paper id 25201468
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IRJET - Automatic Gun Control using Motion Detection System
1. The document describes an automatic gun control system using motion detection that aims to securely protect borders with programmed and computerized methods, reducing human impacts.
2. The system can operate in two modes - automatically tracking targets using image processing algorithms or allowing manual control by the user to select and shoot targets if needed.
3. The process involves using a video camera to capture footage, selecting targets for tracking using image processing techniques like morphological operations, and having a microcontroller decide whether to shoot unauthorized people or activities detected within range by moving the mounted gun and camera.
IMPLEMENTATION OF IDS (INTRUDER DETECTION SYSTEM)
This document describes the implementation of an intruder detection system using a Raspberry Pi, motion detection, facial recognition, and a buzzer system. The system collects face data from homeowners to train a model using CNN techniques. When motion is detected, a picture is taken and facial recognition is run on the Raspberry Pi. If an intruder is identified, the homeowner receives a notification and can choose to sound the buzzer. The system was tested on homeowners and non-homeowners with an average detection time of 5.90 seconds and accuracy of 96.8%. Future work includes integrating an alert box for the homeowner to control the buzzer directly.
IRJET- Applications of Object Detection System
Object detection has a wide range of applications. It can be used for optical character recognition to extract text from documents and images. It is crucial for self-driving cars to detect other vehicles, pedestrians, and road signs. Object detection is also used to track objects in videos like vehicles, people, and sports equipment. Face detection and recognition are common applications and are used by services like Facebook to tag users in photos. Object detection also enables identity verification through biometric technologies like iris scanning. It allows extraction of specific objects from images and videos through image segmentation.
APPLICATION OF VARIOUS DEEP LEARNING MODELS FOR AUTOMATIC TRAFFIC VIOLATION D...
A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
Nano, as the fps is quite low.
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The document describes a proposed method for detecting anomalous activities in videos using deep learning models. It uses a convolutional neural network (CNN) to extract spatial features from video frames. These features are then fed into a bidirectional long short-term memory (LSTM) network to learn temporal features across frames. The CNN-bidirectional LSTM model is trained on normal video activities to learn what normal behavior looks like. During testing, activities that deviate from this learned pattern of normalcy will be flagged as anomalous. The method is evaluated on the UCF Crime dataset and is shown to outperform state-of-the-art approaches for anomalous activity recognition in videos.
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REAL-TIME VIDEO SURVEILLANCE USING RASPBERRY PI
1. The document describes a real-time video surveillance system using a Raspberry Pi 4 and Pi camera. The system is able to monitor video feeds and stream them live in real-time.
2. The system uses OpenCV for coding on the Raspberry Pi operating system. When motion is detected by the camera, an alert is sent along with the captured image.
3. Several previous studies on video surveillance systems using Raspberry Pi are reviewed, covering applications like vehicle detection and tracking, home security systems, and facial recognition systems. The reviewed systems demonstrate high accuracy for tasks like object detection, motion detection, and image classification.
Implementation of-a-motion-detection-system
This document summarizes a research paper that describes the development of a motion detection system using video input. The system is designed to detect motion in live video streams and then activate an alarm and save the video footage. The system was created using MATLAB which allows for video acquisition and processing. When motion is detected between frames, the difference is compared to a threshold. If exceeded, an alarm activates and the video is saved. The goal is to develop surveillance systems that can actively detect and respond to crimes in progress, rather than just passively record evidence afterward.
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IRJET- A Survey on Human Action Recognition
This document discusses and surveys various methods for human action recognition in video surveillance systems. It describes methods for detecting, tracking, and recognizing human actions. For detection, it discusses background subtraction, optical flow, and other techniques. For tracking, it covers region-based, silhouette-based, feature-based, and model-based methods. It also evaluates the merits and limitations of different methods discussed in referenced papers, such as computational efficiency and accuracy. Overall, the document provides an overview of key techniques and challenges in building intelligent video surveillance systems capable of human action understanding.
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Paper id 25201468
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1. The document describes an automatic gun control system using motion detection that aims to securely protect borders with programmed and computerized methods, reducing human impacts.
2. The system can operate in two modes - automatically tracking targets using image processing algorithms or allowing manual control by the user to select and shoot targets if needed.
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This document describes the implementation of an intruder detection system using a Raspberry Pi, motion detection, facial recognition, and a buzzer system. The system collects face data from homeowners to train a model using CNN techniques. When motion is detected, a picture is taken and facial recognition is run on the Raspberry Pi. If an intruder is identified, the homeowner receives a notification and can choose to sound the buzzer. The system was tested on homeowners and non-homeowners with an average detection time of 5.90 seconds and accuracy of 96.8%. Future work includes integrating an alert box for the homeowner to control the buzzer directly.
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A rapid growth in the population and economic growth has resulted in an increasing number of vehicles on
road every year. Traffic congestion is a big problem in every metropolitan city. To reach their destination
faster and to avoid traffic, some people are violating traffic rules and regulations. Violation of traffic rules
puts everyone in danger. Maintaining traffic rules manually has become difficult over the time due to the
rapid increase in the population. This alarming situation has be taken care of at the earliest. To overcome
this, we need a real-time violation detection system to help maintain the traffic rules. The approach is to
detect traffic violations in real-time using edge computing, which reduces the time to detect. Different
machine learning models and algorithms were applied to detect traffic violations like traveling without a
helmet, line crossing, parking violation detection, violating the one-way rule etc. The model implemented
gave an accuracy of around 85%, due to memory constraints of the edge device in this case NVIDIA Jetson
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2. The system uses OpenCV for coding on the Raspberry Pi operating system. When motion is detected by the camera, an alert is sent along with the captured image.
3. Several previous studies on video surveillance systems using Raspberry Pi are reviewed, covering applications like vehicle detection and tracking, home security systems, and facial recognition systems. The reviewed systems demonstrate high accuracy for tasks like object detection, motion detection, and image classification.
Implementation of-a-motion-detection-system
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Detecting anomalies in security cameras with 3D-convolutional neural network ...
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Home work
- 1. Smart City Face recognition base on surveillance monitor and big data that able to track suspects
- 2. Outline ● Introduction ● Body Feasibility analysis. Scientific evidence. How generic people benefited from this idea. ● Reference ● Conclution
- 3. Idea introduction Difference between the approach in nowaday and the conceptual illustration
- 4. Nowaday approch fig (1) Current practices in electronic surveillance 1. 2. 3. 4. Police have to access to all surveillance cameras manually.
- 5. Nowaday approch fig (1.1) Current practices in electronic surveillance 1. 2. 3. 4. 1. Determine where the suspect may appear
- 6. Nowaday approch fig (1.2) Current practices in electronic surveillance 1. 2. 3. 4. 2. Access the surveillance on the path
- 7. Nowaday approch fig (1.3) Current practices in electronic surveillance 1. 2. 3. 4. 3. Capture and organize videos for a specific time period and then assign tasks
- 8. Nowaday approch fig (1.4) Current practices in electronic surveillance 1. 2. 3. 4. 4. Watch video manually and try to find where the suspect is.
- 9. What is the problem? ● Time consuming. ● suspect may run far away during the period of watching video ● human may fail to recognize the face with confusing by wearing different cloth suspect.
- 10. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3.
- 11. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Collect the face into government data management system
- 12. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Machine learning to training facial regonition model.
- 13. The illustration of face recognition in surveillance monitor fig (2) Using machine learning to create face recognition system 1. 2. 3. Cloud system will imform the police where the suspect is, once the system find the specific person.
- 14. Interesting for General Public Safe life Reduce crime rates
- 15. Interesting for General Public Enegy efficiency Saving time
- 16. Interesting for CS/CE Students The active of samrt city bring.. Employment Career Opportunities More funding for research
- 17. Example of SMART CITY technorogy Anylsing data from surveillance cameras Using for tracking offenders ! Using for preventing crime !
- 18. Importantance of IoT ● Improving convenience ● More efficiency ● cost economy
- 19. Example of current IoT technology... ● Door lock system ● air conditioner etc...
- 20. Importance of BIG DATA ● Build database ● Facial recognition
- 21. BIG DATA Management Outline Your Goals Data Protection NEW WAY OLD WAY Adapt to the New Changes
- 25. Challenge Cheaper Device Privacy problem Power Saving IoT
- 26. Reason of Fail Budget Data collection
- 27. Future of SMART CITIES ● More business opportunities
- 29. map
- 30. Refference ● Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN ● BALANCING OF NEEDS BETWEEN :SECURITY AND PRIVACY ENGLAND ● Identify and Trace Criminal Suspects in the Crowd Aided by Fast Trajectories Retrieval ● Face recognition-based real-time system for surveillance