scholarly journals Weapon Detection Using YOLO V3 for Smart Surveillance System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sanam Narejo ◽  
Bishwajeet Pandey ◽  
Doris Esenarro vargas ◽  
Ciro Rodriguez ◽  
M. Rizwan Anjum
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Surveillance System ◽  
Human Life ◽  
Automated System ◽  
Detection Model ◽  
Smart Surveillance ◽  
Computer Based ◽  
Smart Surveillance System ◽  
Detection And Recognition

Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

Prediction of Crime Scene Objects Using Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 857-864
Author(s):  
Vibhavari B Rao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Computer Architecture ◽  
Real Time ◽  
Surveillance System ◽  
Crime Scene ◽  
Detection Model ◽  
Detection Algorithms ◽  
Performance Metric ◽  
Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

Auto Pan Tilt Motion Surveillance System

2012 ◽  
Author(s):  
Pei Song Chee ◽  
Pei Ling Leow ◽  
Mohomad Shukri Abdul Manaf
Keyword(s):  
Motion Detection ◽  
Surveillance System ◽  
Low Cost ◽  
Outdoor Environment ◽  
Automated System ◽  
Maximum Speed ◽  
Camera System ◽  
Smart Surveillance ◽  
Blob Tracking ◽  
Smart Surveillance System

Projek ini bertujuan untuk membangunkan satu sistem yang dilengkapi dengan pelbagai jenis jejakkan. Sistem tersebut kecil dari segi saiz, mudah alih dan memudahkan pemasangan. Konvensyen sistem terdiri daripada fungsi operasi tunggal dan mempunyai sistem yang besar. Projek ini boleh dicapai dengan pemasangan webcam murah dengan dua motor servo yang memainkan peranan sebagai sendi gerakan. Gerakan tersebut berdasarkan proses menarik dan mendorong dengan sambungan yang terlekat pada motor. Sistem tersebut mampu melakukan tugas pengesanan objek, jejakkan warna dan jejakkan gumpalan warna cahaya laser. Selain itu, video rakaman, gambar tangkapan dan pencetusan penggera boleh dilakukan. Sistem pelbagai fungsi ini dibangunkan dengan algoritma gabungan dari pelbagai jenis penapis. Alat tersebut telah dicuba dalam keadaan bilik dan keadaan luaran. Kajian menunjukkan sistem mampu mengkompensasi dengan gangguan hingar. Sistem tersebut mampu mencapai kelajuan 0.125 ms–1 dengan 145° dan 60° periuk gerakan miring. Pemasangan system tersebut melibatkan kos yang murah dan boleh diaplikasikan dalam robot visi, persidangan video dan aplikasi UAV automatik. Kata kunci: Pengesanan gerakan; pelacakan gumpalan warna; pelacakan laser; sistem pemantauan pintar; kamera pelacakan objek This research develops webcam base pan and tilt camera with multiple tracking ability. This pan tilt surveillance system is small in size, portable and easy for installation. Convention surveillance system is limited to single function operation and have bulky camera system. The key component of this surveillance system is the attachment of low cost webcam onto pan and tilt servo motor. The movement of the webcam results from pulls and push coupling unit which attach to the motor. The smart surveillance system able to perform motion detection task, color blob tracking and laser light tracking. Automatic system enhanced its ability into real–time auto motion video record, photo snap shot and trigger alarm. This multi function system is developed with improve algorithm combination from different type of multi–filter. It is experimented under indoor and outdoor environment. The result shows the system able to compensate with the noise disturbance. The reported maximum speed is 0.125 ms–1 with 145° pan movement and 60° tilt movements. Such automated system is cost effective and can be used as robot vision, automated video conference and UAV application. Key words: Motion detection; color blob tracking; laser tracking; smart surveillance system; object tracking camera

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals Remote Fault-Tolerant Control for Industrial Smart Surveillance System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Atif Mahmood ◽  
Abdul Qayyum Khan ◽  
Ghulam Mustafa ◽  
Nasim Ullah ◽  
Muhammad Abid ◽  
...  
Keyword(s):  
Surveillance System ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fault Tolerant Control ◽  
Linear Matrix ◽  
Control Law ◽  
Uncertain Network ◽  
Smart Surveillance ◽  
Communication Links ◽  
Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

scholarly journals IoT Based Smart Surveillance System for Healthcare Monitoring using Raspberry PI

2019 ◽  
Vol 8 (12) ◽  
pp. 627-629
Keyword(s):  
Blood Pressure ◽  
Surveillance System ◽  
Heart Diseases ◽  
Electrical Signal ◽  
Raspberry Pi ◽  
Electronic Board ◽  
Pulse Sensor ◽  
Smart Surveillance ◽  
Healthcare Monitoring ◽  
Smart Surveillance System

IOT could be a trending in technology that can transform any device into a wise one a lot of industries setting out to utilize these technologies to extend their capacity and improve potency. These system has been created to detect people who are suffering with heart diseases, this framework is powered by Raspberry pi electronic board, which is worked on power control supply, Remote web availability by utilizing USB modem, it incorporates with sensors. pulse sensor which detects each beats per minute price. Temperature sensor detects the temperature variation, blood pressure sensor reads blood pressure and heart rate, ECG sensor which measures the electrical signal of the heart. it is an analog from converted in digital by using of SPI protocol. If any emergency occurs, it will raise a caution send it to the website and mobile though NOOBS Software. If any sensor parameter value more than the instructed value it will raise a beep sound

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