1) The document presents a system for detecting militants and weapons in images using machine learning. It aims to automatically detect dangerous situations by identifying knives, firearms, and militants in CCTV footage.
2) The proposed system uses a YOLO convolutional neural network model trained on a dataset of annotated images. It extracts features from images and uses the trained model to detect militants and classify weapon types in real-time video streams.
3) If militants or weapons are detected, the system alerts security operators. It is intended to reduce operator workload from monitoring multiple CCTV feeds and enhance security by automating threat detection.
This document describes the development of an embedded Linux-based navigation system for an autonomous underwater vehicle (AUV). It discusses interfacing a single board computer (SBC) with navigation sensors like an inertial measurement unit (IMU) and GPS. It also describes implementing a Kalman filter on the SBC to fuse sensor data and estimate the AUV's position. Software was developed on the SBC to log and display sensor outputs. The system provides low-cost navigation for AUVs and can be used for marine research and applications like pipeline inspection.
YOLOv4: optimal speed and accuracy of object detection reviewLEE HOSEONG
YOLOv4 builds upon previous YOLO models and introduces techniques like CSPDarknet53, SPP, PAN, Mosaic data augmentation, and modifications to existing methods to achieve state-of-the-art object detection speed and accuracy while being trainable on a single GPU. Experiments show that combining these techniques through a "bag of freebies" and "bag of specials" approach improves classifier and detector performance over baselines on standard datasets. The paper contributes an efficient object detection model suitable for production use with limited resources.
Unmanned aerial vehicles (UAVs), commonly known as drones, have evolved from early prototypes in the early 20th century to modern multi-purpose aircraft. Drones range in size from small "micro" drones weighing less than 10kg to larger "heavy" drones over 1000kg. They can be remotely piloted or fly autonomously using satellite navigation. While initially developed for military purposes like reconnaissance and targeting, drones are increasingly used for civilian applications such as fire monitoring, mapping, and aerial photography.
In accordance with the present dependence on UAVs and Drones, these Unmanned Aerial Vehicles have proved them a great asset. These UAVs are expected to serve a great role in almost every field like military, agriculture, police, disaster management, industrial management, educational field etc. in the coming future which has been described in the given slides.
The document provides an introduction to computer vision concepts including neural network structures, activation functions, convolution operators, pooling layers, and batch normalization. It then discusses image classification, including popular datasets, classification networks from LeNet to DLA, and experiments on car brand classification. Finally, it covers object detection, comparing region-based methods like R-CNN, Fast R-CNN, Faster R-CNN, and R-FCN to region-free methods like YOLO.
Drones, also known as unmanned aerial vehicles (UAVs), can be used for both commercial and military purposes. They are aircraft controlled remotely or through autonomous systems. Drones are equipped with cameras, sensors and other technologies to perform tasks such as aerial photography, surveillance, product delivery and more. While drones provide advantages like low costs, risks to human life and operational flexibility, they also raise issues regarding privacy, civilian casualties and potential misuse if their abilities are not properly regulated.
This document describes the development of an embedded Linux-based navigation system for an autonomous underwater vehicle (AUV). It discusses interfacing a single board computer (SBC) with navigation sensors like an inertial measurement unit (IMU) and GPS. It also describes implementing a Kalman filter on the SBC to fuse sensor data and estimate the AUV's position. Software was developed on the SBC to log and display sensor outputs. The system provides low-cost navigation for AUVs and can be used for marine research and applications like pipeline inspection.
YOLOv4: optimal speed and accuracy of object detection reviewLEE HOSEONG
YOLOv4 builds upon previous YOLO models and introduces techniques like CSPDarknet53, SPP, PAN, Mosaic data augmentation, and modifications to existing methods to achieve state-of-the-art object detection speed and accuracy while being trainable on a single GPU. Experiments show that combining these techniques through a "bag of freebies" and "bag of specials" approach improves classifier and detector performance over baselines on standard datasets. The paper contributes an efficient object detection model suitable for production use with limited resources.
Unmanned aerial vehicles (UAVs), commonly known as drones, have evolved from early prototypes in the early 20th century to modern multi-purpose aircraft. Drones range in size from small "micro" drones weighing less than 10kg to larger "heavy" drones over 1000kg. They can be remotely piloted or fly autonomously using satellite navigation. While initially developed for military purposes like reconnaissance and targeting, drones are increasingly used for civilian applications such as fire monitoring, mapping, and aerial photography.
In accordance with the present dependence on UAVs and Drones, these Unmanned Aerial Vehicles have proved them a great asset. These UAVs are expected to serve a great role in almost every field like military, agriculture, police, disaster management, industrial management, educational field etc. in the coming future which has been described in the given slides.
The document provides an introduction to computer vision concepts including neural network structures, activation functions, convolution operators, pooling layers, and batch normalization. It then discusses image classification, including popular datasets, classification networks from LeNet to DLA, and experiments on car brand classification. Finally, it covers object detection, comparing region-based methods like R-CNN, Fast R-CNN, Faster R-CNN, and R-FCN to region-free methods like YOLO.
Drones, also known as unmanned aerial vehicles (UAVs), can be used for both commercial and military purposes. They are aircraft controlled remotely or through autonomous systems. Drones are equipped with cameras, sensors and other technologies to perform tasks such as aerial photography, surveillance, product delivery and more. While drones provide advantages like low costs, risks to human life and operational flexibility, they also raise issues regarding privacy, civilian casualties and potential misuse if their abilities are not properly regulated.
Drones have been used militarily since the early 20th century but have become more advanced and widespread in recent decades. The document outlines the history of drones and their various types, including how they differ based on size, range, aerial platform, and abilities. Drones provide advantages for tasks like surveillance and videography but also have disadvantages like potential restrictions on their use and risks of violating privacy laws.
Drone Detection & Classification using Machine LearningIRJET Journal
This document summarizes a research paper on drone detection and classification using machine learning. It discusses how drone technology is used for various purposes like food delivery and emergency response. The research focuses on using machine learning and image processing for drone surveillance in high-risk areas. It describes the system architecture, which uses sensors like thermal cameras, video cameras, and microphones along with a machine learning model on a laptop. The document outlines the methodology, including how the sensors are mounted and integrated into the system. It also discusses the software and algorithms used for detection, classification and tracking of drones.
[PR12] You Only Look Once (YOLO): Unified Real-Time Object DetectionTaegyun Jeon
The document summarizes the You Only Look Once (YOLO) object detection method. YOLO frames object detection as a single regression problem to directly predict bounding boxes and class probabilities from full images in one pass. This allows for extremely fast detection speeds of 45 frames per second. YOLO uses a feedforward convolutional neural network to apply a single neural network to the full image. This allows it to leverage contextual information and makes predictions about bounding boxes and class probabilities for all classes with one network.
The document describes a social distancing detection system that uses the YOLO object detection algorithm and COCO dataset to detect people in video frames and estimate distances between them. It draws bounding boxes around detected people, with violations of the default distance threshold shown in red and non-violations in green. The number of violations and alert messages are displayed on screen to help users maintain safe distances. The system was tested on prerecorded video and images and was able to accurately detect violations and maintain social distancing.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Yolo is an end-to-end, real-time object detection system that uses a single convolutional neural network to predict bounding boxes and class probabilities directly from full images. It uses a deeper Darknet-53 backbone network and multi-scale predictions to achieve state-of-the-art accuracy while running faster than other algorithms. Yolo is trained on a merged ImageNet and COCO dataset and predicts bounding boxes using predefined anchor boxes and associated class probabilities at three different scales to localize and classify objects in images with just one pass through the network.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
UAVs, or unmanned aerial vehicles, are aircraft that can fly without a human pilot onboard. They are controlled remotely or can be programmed to fly autonomously. UAVs have been developed for both military and civilian uses such as reconnaissance, surveillance, cargo delivery and more. The document provides a detailed history of UAV development from their origins in the early 20th century to modern applications.
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
Power Presentation on UAVs.Basically covering all the informative topics related to UAVs.Starting from different terminology and ending up to future vision and advantages.
It is actually a fully made presentation one can directly use to present it.It contains pictures so by the use of it one can able to understand each and every line in the particular slide.
Counter-UAS: Legal Challenges and Solutions for Research and Development (Jac...DroneSec
Jacob Tewes (Kutak Rock)
Talk Recording: https://www.youtube.com/watch?v=JUQh6DS51Uw
The Global Drone Security Network (GDSN) is the only event of its kind focusing on Cyber-UAV security, Drone Threat Intelligence, Counter-UAS, and UTM security. Watch the full recording here: https://www.youtube.com/watch?v=vZ6sRr65cSk
Speaker: https://www.linkedin.com/in/jacob-tewes-b20b017/
DroneSec is a cyber-uav security and threat intelligence company who hosted this second series of the GDSN community event.
https://dronesec.com/
Drones are unmanned aerial vehicles that have no pilot onboard. The document summarizes the key parts of drones including the airframe, propulsion system, flight control computer, and payload accessories. It describes how drones can be controlled through radio signals for short distances or via satellites and ground control stations for longer distances. Examples of drone applications include military uses like reconnaissance as well as civilian uses in agriculture, climate monitoring, and deliveries. The future of drone technology is predicted to include expanded uses in farming, archaeology, humanitarian efforts, and more.
Towards An Open Instrumentation Platform: Getting The Most From MAVLink, Ardu...Steve Arnold
What is a drone? What is an autopilot, and just what is an IMU and a Kalman filter? This presentation describes an open source hardware and software architecture defined by the Ardupilot firmware, the MAVLink message protocol, several layers of user-space software, and various supported hardware devices and peripherals. It will also cover the current capabilities and components of the core software stacks, as well as extended support for different hardware platforms and sensors, computer vision processing, cameras and image tags, as well as specific science applications and related FOSS projects currently underway. The two highlighted projects both suggest more non-traditional (and less mobile) data acquisition applications using these tools; for more typical UAV applications, airframe options and alternative firmware will also be discussed.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Drone simulators, advancements and challengesNile University
The presentation describes the drones' simulators and compares their capabilities and usages with examples.
Also, how to select the proper simulator and basic requirements and challenges.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
The document discusses drones/unmanned aerial vehicles (UAVs), including definitions, examples of drones in use today, and their applications. Some key points are:
- Drones are powered, aerial vehicles that can fly autonomously or be piloted remotely, and can carry payloads like weapons or cameras.
- Common drones include the Solar Eagle, Puma AE, Predator, Nano, Hummingbird, and quadcopters.
- Drones are used commercially, by governments/research, and as hobbies for purposes like surveillance, mapping, search and rescue, and delivery.
- Regulations in India require drones to fly below 400 feet and only during daylight hours to avoid disturbing
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)cscpconf
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Drones have been used militarily since the early 20th century but have become more advanced and widespread in recent decades. The document outlines the history of drones and their various types, including how they differ based on size, range, aerial platform, and abilities. Drones provide advantages for tasks like surveillance and videography but also have disadvantages like potential restrictions on their use and risks of violating privacy laws.
Drone Detection & Classification using Machine LearningIRJET Journal
This document summarizes a research paper on drone detection and classification using machine learning. It discusses how drone technology is used for various purposes like food delivery and emergency response. The research focuses on using machine learning and image processing for drone surveillance in high-risk areas. It describes the system architecture, which uses sensors like thermal cameras, video cameras, and microphones along with a machine learning model on a laptop. The document outlines the methodology, including how the sensors are mounted and integrated into the system. It also discusses the software and algorithms used for detection, classification and tracking of drones.
[PR12] You Only Look Once (YOLO): Unified Real-Time Object DetectionTaegyun Jeon
The document summarizes the You Only Look Once (YOLO) object detection method. YOLO frames object detection as a single regression problem to directly predict bounding boxes and class probabilities from full images in one pass. This allows for extremely fast detection speeds of 45 frames per second. YOLO uses a feedforward convolutional neural network to apply a single neural network to the full image. This allows it to leverage contextual information and makes predictions about bounding boxes and class probabilities for all classes with one network.
The document describes a social distancing detection system that uses the YOLO object detection algorithm and COCO dataset to detect people in video frames and estimate distances between them. It draws bounding boxes around detected people, with violations of the default distance threshold shown in red and non-violations in green. The number of violations and alert messages are displayed on screen to help users maintain safe distances. The system was tested on prerecorded video and images and was able to accurately detect violations and maintain social distancing.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Yolo is an end-to-end, real-time object detection system that uses a single convolutional neural network to predict bounding boxes and class probabilities directly from full images. It uses a deeper Darknet-53 backbone network and multi-scale predictions to achieve state-of-the-art accuracy while running faster than other algorithms. Yolo is trained on a merged ImageNet and COCO dataset and predicts bounding boxes using predefined anchor boxes and associated class probabilities at three different scales to localize and classify objects in images with just one pass through the network.
AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM FOR INDIAN VEHICLE IDENTIFICATION ...Kuntal Bhowmick
Automatic License Plate Recognition (ANPR) is a practical application of image processing which uses number (license) plate is used to identify the vehicle. The aim is to design an efficient automatic vehicle identification system by using the
vehicle license plate. The system is implemented on the entrance for security control of a highly restricted area like
military zones or area around top government offices e.g.Parliament, Supreme Court etc.
It is worth mentioning that there is a scarcity in researches that introduce an automatic number plate recognition for indian vechicles.In this paper, a new algorithm is presented for Indian vehicle’s number plate recognition system. The proposed algorithm consists of two major parts: plate region extraction and plate recognition.Vehicle number plate region is extracted using the image segmentation in a vechicle image.Optical character recognition technique is used for the character recognition. And finally the resulting data is used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, registration state, address, etc.
The performance of the proposed algorithm has been tested on real license plate images of indian vechicles. Based on the experimental results, we noted that our algorithm shows superior performance special in number plate recognition phase.
UAVs, or unmanned aerial vehicles, are aircraft that can fly without a human pilot onboard. They are controlled remotely or can be programmed to fly autonomously. UAVs have been developed for both military and civilian uses such as reconnaissance, surveillance, cargo delivery and more. The document provides a detailed history of UAV development from their origins in the early 20th century to modern applications.
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology
* Object
* Object Tracking
* Application
* Background Study
* How it works
* Multi-Object Tracking
* Solution
* Future Works
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
Power Presentation on UAVs.Basically covering all the informative topics related to UAVs.Starting from different terminology and ending up to future vision and advantages.
It is actually a fully made presentation one can directly use to present it.It contains pictures so by the use of it one can able to understand each and every line in the particular slide.
Counter-UAS: Legal Challenges and Solutions for Research and Development (Jac...DroneSec
Jacob Tewes (Kutak Rock)
Talk Recording: https://www.youtube.com/watch?v=JUQh6DS51Uw
The Global Drone Security Network (GDSN) is the only event of its kind focusing on Cyber-UAV security, Drone Threat Intelligence, Counter-UAS, and UTM security. Watch the full recording here: https://www.youtube.com/watch?v=vZ6sRr65cSk
Speaker: https://www.linkedin.com/in/jacob-tewes-b20b017/
DroneSec is a cyber-uav security and threat intelligence company who hosted this second series of the GDSN community event.
https://dronesec.com/
Drones are unmanned aerial vehicles that have no pilot onboard. The document summarizes the key parts of drones including the airframe, propulsion system, flight control computer, and payload accessories. It describes how drones can be controlled through radio signals for short distances or via satellites and ground control stations for longer distances. Examples of drone applications include military uses like reconnaissance as well as civilian uses in agriculture, climate monitoring, and deliveries. The future of drone technology is predicted to include expanded uses in farming, archaeology, humanitarian efforts, and more.
Towards An Open Instrumentation Platform: Getting The Most From MAVLink, Ardu...Steve Arnold
What is a drone? What is an autopilot, and just what is an IMU and a Kalman filter? This presentation describes an open source hardware and software architecture defined by the Ardupilot firmware, the MAVLink message protocol, several layers of user-space software, and various supported hardware devices and peripherals. It will also cover the current capabilities and components of the core software stacks, as well as extended support for different hardware platforms and sensors, computer vision processing, cameras and image tags, as well as specific science applications and related FOSS projects currently underway. The two highlighted projects both suggest more non-traditional (and less mobile) data acquisition applications using these tools; for more typical UAV applications, airframe options and alternative firmware will also be discussed.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Drone simulators, advancements and challengesNile University
The presentation describes the drones' simulators and compares their capabilities and usages with examples.
Also, how to select the proper simulator and basic requirements and challenges.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
The document discusses drones/unmanned aerial vehicles (UAVs), including definitions, examples of drones in use today, and their applications. Some key points are:
- Drones are powered, aerial vehicles that can fly autonomously or be piloted remotely, and can carry payloads like weapons or cameras.
- Common drones include the Solar Eagle, Puma AE, Predator, Nano, Hummingbird, and quadcopters.
- Drones are used commercially, by governments/research, and as hobbies for purposes like surveillance, mapping, search and rescue, and delivery.
- Regulations in India require drones to fly below 400 feet and only during daylight hours to avoid disturbing
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)cscpconf
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. The document proposes a system using drones to detect garbage and alert cleaners. A detector drone will fly over an area, take photos, and send them to a server. The server uses computer vision to identify garbage and alerts the nearest cleaner.
2. A checker drone then verifies that the garbage has been cleaned. It flies to locations where garbage was detected and uses computer vision to confirm if cleaning was completed. This system aims to help keep areas clean as part of India's Swachh Bharat mission.
3. The detector drone takes photos with a camera and GPS and sends them to a server. The server uses a convolutional neural network model trained on garbage images to identify garbage in the photos. If
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)csandit
The proposed work aims to create a smart application camera, with the intention of eliminating
the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at
arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in
OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen
Faces and the test images are verified by using distance based algorithm against the eigenfaces,
like Euclidean distance algorithm or Mahalanobis Algorithm.
If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an
alarm signal is raised.
This document summarizes a research paper on a webcam-based intelligent surveillance system. The system uses a webcam as a sensor to capture live images of the monitored area. If any motion is detected in the images, the software stores the captured images in a folder. It also sends a wireless signal to a receiver. The system has three security levels - high, middle, and low - to control the monitoring sensitivity. The key modules are login, camera interfacing, image capturing and storage, motion detection, and hardware interfacing. Motion detection works by comparing images over time and detecting changes. The system is presented as an affordable alternative to other security systems like CCTV cameras that have drawbacks around costs and vulnerability to hacking.
IRJET- Smart Traffic Control System using YoloIRJET Journal
This document describes a proposed smart traffic control system that uses the YOLO object detection algorithm and deep learning techniques. The system uses a camera to capture real-time video of a road intersection. The YOLO algorithm running on a server analyzes the video to detect vehicles and determine traffic flow characteristics like queue density. It then optimizes the traffic signal phases to minimize wait times and maximize the number of vehicles that can safely pass through the intersection. The deep learning model is deployed on an embedded controller using transfer learning to allow for object detection with limited hardware resources. The goal is to develop an intelligent traffic light control system that can dynamically adjust signal timing based on real-time traffic conditions.
CRIMINAL IDENTIFICATION FOR LOW RESOLUTION SURVEILLANCEvivatechijri
Criminal Identification System allows the user to identify a certain criminal based on their biometrics. With advancements in security technology, CCTV cameras have been installed in many public and private areas to provide surveillance activities. The CCTV footage becomes crucial for understanding of the criminal activities that take place and to detect suspects. Additionallywhen a criminal is found it is difficult to locate and track him with just his image if he is on the run. Currently this procedure consists of finding such people in CCTV surveillance footage manually which is time consuming. It is also a tedious process as the resolution for such CCTV cameras is quite low. As a solution to these issues, the proposed system is developed to go through real time surveillance footage, detect and recognize the criminals based on reference datasets of criminals. The use of facial recognition for identifying criminals proves to bebeneficial. Once the best match is found the real time cropped image of the recognized criminal is saved which can be accessed by authorized officials for locating and tracking criminals or for further investigative use.
Trajectory Based Unusual Human Movement Identification for ATM SystemIRJET Journal
This document summarizes a research paper on developing a system to identify unusual human movements at ATMs using trajectory analysis. The proposed system uses computer vision techniques like background subtraction and template matching to detect and track human movements. If a person's trajectory does not match expected patterns or if their face is covered, an alarm is triggered. The system is intended to prevent crimes and unauthorized access at ATMs by continuously monitoring movements and alerting administrators of any suspicious activity in real-time.
Human Motion Detection in Video Surveillance using Computer Vision TechniqueIRJET Journal
The document discusses a technique for detecting human motion in video surveillance using computer vision. It proposes a method called DECOLOR (Detecting Contiguous Outliers in the LOw-rank Representation) that formulates object detection as outlier detection in a low-rank representation of video frames. This allows it to detect moving objects flexibly without assumptions about foreground or background behavior. DECOLOR simultaneously performs object detection and background estimation using only the test video sequence, without requiring training data. The method models the outlier support explicitly and favors spatially contiguous outliers, making it suitable for detecting clustered foreground objects like people. It achieves more accurate detection and background estimation than state-of-the-art robust principal component analysis methods.
This document summarizes a research paper on developing a smart web cam motion detection surveillance system using a low-cost webcam. The system aims to detect intruders and stop intrusions in progress. It does this by comparing frames from the webcam video stream to detect motion. When motion is detected, it sounds an alarm, emails photos of the intruder to the administrator, and sends an SMS alert. The system is designed to be low-cost by using a webcam instead of more expensive security cameras. It outlines the system architecture, including capturing video, comparing frames to detect motion, storing motion frames, and alerting the administrator. It also discusses related work on motion detection and implementation details like background/foreground separation.
This document summarizes a project on real-time object detection using computer vision techniques. It discusses using a system that can recognize objects in a video stream from a camera and label them with bounding boxes and labels. It notes that most video surveillance footage is uninteresting unless there are moving objects. The project aims to address this by building an accurate, fast object detection system that can run on resource-constrained devices. It proposes using a hybrid CNN-SVM model trained on a large dataset to recognize objects and discusses the training and detection phases of the system.
Real Time Object Dectection using machine learningpratik pratyay
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army target detection using machine learning
1. DR. SRI. SRI. SRI. SHIVAKUMARA MAHASWAMY COLLEGE
OF ENGINEERING
BYRANAYAKANAHALLI,NELAMANGALATQ.BANGALORERURALDIST.-562132
2022–2023
“MILITANT INTRUSION DETECTION USING
MACHINE LEARNING”
ASHOKA M [1CC19CS007]
RAGHUNATHA T R [1CC19CS032]
PRANAV V S [1CC19CS031]
DRUWA KUMAR C [1CC19CS018]
Presented By Under the Guidance of
Miss. Vinaya D S, B.E, M Tech
Assistant Professor
Department of computer science and Engineering
2. AGENDA
Introduction
Problem Statement
Literature survey
Objectives
Existing System
Proposed System
System Design
High Level Design
Implementation Modules
Conclusion
References
3. Introduction
When An Individual Carries A Weapon (Firearm Or A Knife) Out In The Open, It Is A Strong Indicator Of A
Potentially Dangerous Situation. While Some Countries Allow For Open Carry Firearms, In Such An Event,
It Is Still Advisable To Grab The CCTV Operators’ Attention In Order To Assess The Situation At Hand.
During Recent Years, An Increase In The Number Of Incidents With The Use Of Dangerous Automated
Methods For Video Surveillance Have Started To Emerge In Recent Years, Mainly For The Purpose Of
Intelligent Transportation Systems (ITS).
We Have Focused On The Specific Task Of Automated Detection And Recognition Of Dangerous Situations
Applicable In General For Any Cctv System. The Problem We Are Tackling Is The Automated Detection Of
Dangerous Weapons—knives And Firearms, The Most Frequently Used And Deadly Weapons. The
Appearance Of Such Objects Held In A Hand Is An Example Of Assign Of Danger To Which The Human
Operator Must Be Alerted
4. Introduction (CONT….)
Closed Circuit Television Systems (CCTV) Are Becoming More And More Popular And Are
Being Deployed In Many Offices, Housing Estates And In Most Public Spaces. There Are A
Million Of CCTV Cameras That Are Currently In Operation In India. This Makes For An
Enormous Load For The CCTV Operators, As The Number Of Camera Views A Single Operator
Can Monitor Is Limited By Human Factors. The Task Of The CCTV Operator Is To Monitor And
Control, Detect, Observe, Recognize And Identify Individuals And Situations That Are Potentially
Harmful To Other People And Property But It Becomes Harder To Monitor When There Are A
Lot Of CCTV Cameras.
A Solution To The Problem Of Overloading The Human Operatories To Apply Automated Image-
understanding Algorithms, Which, Rather Than Substituting The Human Operator, Alert Them If
A Potentially Dangerous Situation Is At Hand
5. Problem Statement
To Design And Implement A System To Detect The Weapon And Militant In The Given Image And Answer
The Question Related To Image.
INPUT: Image Containing Weapon And Militant. Process:
• The Processing Consists Of Identification Of The Individual Component Part Of The Weapon
And Militant By Using CNN Algorithm .
• After Identification If Any Weapon And Militants Are Found That Will Be Detected.
OUTPUT: Display Weapon And Militant Type When Weapon And Militant Are Detected It Intimates The
Admin Side Of User.
8. Objectives
Detection Of Human Image And Criminal Identification
Detection Of Segment Containing The Weapon In The Detected Image Segment Containing Human
Extraction Of Features Of Each Segment
Design Of A Neural Network Based Classifier To Classify A Single Type Of Weapon Verses Non-
weapon
Computation Image Required To Detect The Weapon Directly In A Image As Compared To Detection
Of Weapon After The Human Detection.
9. Existing System And Drawbacks:
The Existing Systems Does Not Classify Normal And Abnormal Events Leading The Police To
Become More Reluctant To Attend The Crime Scenes Unless There Was A Visual Verification,
Either By Manned Patrols Or By Electronic Images From The Surveillance Camera.
The System Is Done With The Image Classification Model Using Cnn With The Concept Of
Sequential Models And Yolov3 Model In The Darknet Framework
Drawbacks:
The Processing Speed Of The Existing System And Accuracy Will Be Low .
Running Under The Darknet Framework Which Is Made By C Language
Background Junk Detections Were High.
10. Problem Identification
Nowadays Protection For Personal And Personal Property Becoming Very Important. Video Surveillance
Gives A Good Role In Real-time.
Because Of These Needs Deployment Of Cameras Take Place At Every Corner, Video Surveillance System
Understand The Scene And It Automatically Detects Abnormal Activities.
To Recognize The Occurrence Of Uncommon Events Such As Unknown Or With Weapon Or Grenades Or
Tankers Detects In The Low Resolution Video Simply By Using Statistical Property, Standard Deviation
Of Moving Objects.
Detection And Getting Details About The Detection Is Logically Hard To Get Implement With Efficient
Output.There Are Lots Of Techniques To Detect The Different Types Of Anomaly Detection In Surveillance.
Conventional Methods Of Detecting Intrusion In Naked Eye Observation In Live Time Detection.
11. Proposed System
Here We Proposes A System For Militant And Military Object Detection Using Yolov5 And Python. The
Project Involves Collecting A Large Dataset Of Annotated Images Of Military Objects And Militants,
Splitting The Dataset Into Training, Validation, And Testing Sets, And Converting The Annotated Images
Into Yolov5 Format. The Yolov5 Model Is Then Trained On The Annotated Dataset And Evaluated Using
Various Metrics Such As Precision, Recall, And Map .
Once The Model Is Trained And Evaluated, It Is Deployed To Detect Militants And Military Objects In Real-
time Images Or Videos Using Python Libraries Like Opencv Or Pytorch . The Proposed System Can Also Be
Integrated With Additional Features Such As Object Tracking, Alert Notifications, Or Automatic Response
Mechanisms To Enhance Its Functionality. The Project Demonstrates The Potential Of Deep Learning-based
Object Detection For Military And Defense Applications And Serves As A Basis For Further Research In
This Area.
12. System Design
The System Architecture For The Proposed System. The Input Image
Is Preprocessed And Converted To Gray Scale Image To Get The
Clear Vision Of The Image. Then It Will Be Converted Into Binary
Values. In The Next Step Identifies The Part Which Needs To
Proceed Further.
Then Required Feature Are Extracted By In The Cnn Convolution
Layer. By Passing Those Features Into Different Layer Of CNN We
Get Compressed Image, That Feature Is Used For Detection Of
Weapon And Militant Using Softmax Activation Function.
14. Yolo Architecture
For Detecting Militant Data Is Trained Using Yolo Model. YOLO, In A Single Glance, Takes The Entire
Image And Predicts For These Boxes The Bounding Box Coordinates And Class Probabilities. Yolo's
Greatest Advantage Is Its Outstanding Pace, It's Extremely Fast, And It Can Handle 45 Frames Per
Second .Amongst The Three Versions Of YOLO 3 And 5, Is Fastest And More Accurate In Terms Of
Detecting Small Objects. The Proposed Algorithm, YOLO Consists Of Total 106 Layers . The
Architecture Is Made Up Of 3 Distinct Layer Forms. Firstly, The Residual Layer Which Is Formed
When Activation Is Easily Forwarded To A Deeper Layer In The Neural Network. In A Residual Setup,
Outputs Of Layer 1 Are Added To The Outputs Of Layer 2. Second Is The Detection Layer Which
Performs Detection At 3 Different Scales Or Stages. Size Of The Grids Is Increased For Detection. Third
Is The Up-sampling Layer Which Increases The Spatial Resolution Of An Image. Here Image Is Up
Sampled Before It Is Scaled. Also, Concatenation Operation Is Used, To Concatenate The Outputs Of
Previous Layer To The Present Layer. Addition Operation Is Used To Add Previous Layers. In The Fig 3,
The Pink Colored Blocks Are The Residual Layers, Orange Ones Are The Detection Layers And The
Green Are The Up-sampling Layers. Detection At Three Different Scales Is As Shown Fig.
15. Data flow diagram
A Data Flow Diagram (DFD) Is Graphic Representation Of The "Flow" Of Data Through An Information
System.
A Data Flow Diagram Can Also Be Used For The Visualization Of Data Processing (Structured Design).
It Is Common Practice For A Designer To Draw A Context Level Dfd First Which Shows The Interaction
Between The System And Outside Entities.
16. This Level Of Preprocessing Shows That The Image Is Given As Input.
As We Giving The Color Image So That Rgb Image Is Converted Into Gray Scale Values To Reduce
Complexity In The Image.
For Efficient Feature Extraction Gray Scale Values Are Converted Into Binary Values.
Then The Image With Reduced Complexity Is Send To The Next Process.
17. The Figure Of Identification Shows That The Image With Reduced Complexity Is Considered As Input.
Here The Region With The Value Of One Is Considered As Black That Region Is Considered For Next
Process.
18. Shows That The Region Of Interest From The Identification Step Is Considered As Input. The Region Of
Interest Is Obtained From Converting RGB Color Image To The Gray Scale Image By Using Minmax Scalar
Method.
For That Region Cnn Algorithm Is Applied. A CNN Consists Of An Input Layer And An Output Layer, As
Well As Multiple Hidden Layers Between Them.
The Hidden Layer Basically Consists Of The Convolution Layer, Pooling Layer, Relu Layer And Fully
Connected Layers.
19. Shows That The One-dimension Array Is Send To Fully Connected Layer Of CNN. Artificial Neural
Network Method Is Applied To This Layer.
Firstly, One-dimension Array Is Sent To Input Layer. Some Particular Feature Which Is Required For The
Detection Is Identified By The Hidden Layer Of ANN.
The Continue Connection From Hidden Layer To Output Layer Will Help To Identify Accurate Result. By
Considering All The Features Output Layer Gives The Result With Some Predictive Value. These Values Are
Calculated By Using Softmax Activation Function.
Softmax Activation Function Provides Predictive Values. Based On The Prediction Value The Final Result
Will Be Identified. The Highest Value Of Prediction Is Identified As Weapon And Militant.
20. class diagram
Class Diagram Describes The Attributes And Operations Of A Class And Also The Constraints Imposed On
The System. The Class Diagrams Are Widely Used In The Model Ingo Object Oriented Systems Because
They Are The Only UML Diagrams, Which Can Be Mapped Directly With Object-oriented Languages.
The Purpose Of Fig Is To Model The Static View Of An Application By Using Class Diagram Are The Only
Diagram Which Can Be Directly Mapped With Object–oriented Languages And Thus Widely Used At The
Time Of Construction.
21. Module Split-ups
1.Image Pre Processing.
2.Identification
3.Feature Extraction
4.Weapon and militant Recognition
5.Militant Detection
6.Intimation
22. Image Pre-processing
Image Processing Is A Mechanism That Focuses On The Manipulation Of Images In Different Ways In Order
To Enhance The Image Quality. Images Are Taken As The Input And Output For Image Processing
Techniques. It Is The Analysis Of Image To Image Transformation Which Is Used For The Enhancement Of
Image
23. Identification
In This Stage Identify The Region Which Needs To Proceed For Further Process, It Is Involved In The
Identification Of The Particular Region Of The Image That Is Used For The Further Process Like Feature
Extraction And Classification Of The Images.
The Output Of The Pre-processing Step Is Given As The Input For The Identification Process. This Process Is
Based On The Binary Values Obtained In The Pre-processing Step. The Region With Black Is Considered A
Region Of Interest. The Region Of Interest Obtained By The Pre-processing Of The Images. That Region Is
Considered As Proceeding Part Of The Image From Which Weapon And Militant Will Be Identified. The
Identified Weapon And Militant Images Are Given To The Feature Extraction Process
24. Feature Extraction
In This Stage Extract The Required Feature From The Identified Region Which Is Obtained From The
Previous Step. That Region Is Compressed By Converting A Reduced Size Matrix To Control Over Fitting.
The Reduction Of The Matrix Size Helps In Reducing The Memory Size Of The Images. Then The Flattening
Process Is Applied To The Reduced Matrix, In Which The Reduced Matrix Is Converted To A One-dimension
Array, Which Is Used For Final Detection.
25. Weapon and militant Recognition
In This Stage One-dimension Array Is Used For The Final Classification Process. The Output Image
Obtained From Feature Extraction Is Given As Input To This Process. Where Continuous Classification Of
All The Features Obtained From The Previous Stage. Artificial Neural Networks Are Applied In This
Process.
Each Node Of The Input Layer Has A Value From A One Dimension Array Which Represents The Feature
From The Extracted Region.
That Is Sent To The Hidden Layer. Multiple Features Are Getting From The Input Layer And Undergo
Multiple Iteration In The Hidden Layer.
Finally Get The Predictive Values By Applying SoftMax Activation Function To It. Finally, Get Some
Output Values From This Process And These Values Undergo Further Process.
The Highest Value In The Predictive Value Is Considered As Output Identified As Weapon And Militant. By
Using These Methods, The Weapon And Militant Will Be Detected By Considering Highest Accuracy
Values.
26. Intimation
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27. Conclusion
The Proposed Project Aimed To Develop A Deep Learning Model That Can Detect Militants In Real-time
Using The YOLO Algorithm. The Project Was Implemented In Five Modules, Including Dataset Preparation,
Yolov3 Model Training, Real-time Detection, Performance Evaluation, And Voice Announcement.
The Project Has Potential Applications In The Security And Defense Sectors, Where The Detection Of
Militants Is Of Critical Importance. The Model Was Trained And Evaluated Using Precision, Recall, And F1
Score Metrics, And The Results Showed High Accuracy In Detecting Militants In Real-time.
The Voice Announcement System Was Integrated With The Real-time Detection Module, Which Provided An
Additional Layer Of Security And Alerted The Security Personnel Immediately. The Project Has The Potential
To Enhance The Security And Defense Sectors By Providing A Fast And Accurate Militant Detection System.
28. REFERENCES
Harsha Jain Et.Al. “Weapon And Militant Detection Using Artificial Intelligence And Deep
Learning For Security Applications” ICESC 2020.
Arif Warsi Et.Al “Automatic Handgun And Knife Detection Algorithms” IEEE Conference
2019.
Neelam Dwivedi Et.Al. “Weapon And Militant Classification Using Deep Convolutional
Neural Networks” IEEE Conference CICT 2020.
Kumar Verma Et.Al. “Handheld Gun Detection Using Faster RCNN Deep Learning” IEEE
Conference 2019.
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