scholarly journals IoT Based Surveillance System Using DNN Model

2021 ◽  
Vol 9 (12) ◽  
pp. 35-41
Author(s):  
Rahul Rawat
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Surveillance System ◽  
Armed Forces ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Detection Model ◽  
Log Files ◽  
Loss And Damage ◽  
Communication Module

Abstract: Localization, Visibility, Proximity, Detection, Recognition has always been a challenge for surveillance system. These challenges can be felt in the industries where surveillance systems are used like armed forces, technical-agriculture and other such fields. Most of the Smart system available are just for the surveillance of Human intervention but there is a need for a system which can be used for animals as well because with the outburst of human population and symbiotic relationship with wild animals results in life loss and damage to agriculture. In this paper we are designing to overcome these above-mentioned challenges for human and animal-based surveillance system in real time application. The system setup is done on a Raspberry pi integrated with deep-learning models which performs the classification of objects on the frames, then the classified objects is given to a face detection model for further processing. The detected face is relayed to the back-end for feature mapping with the saved log files with containing features of familiar face IDs. Four models were tested for face detection out of which the DNN model performed the best giving an accuracy of 94.88%.The system is also able to send alerts to the admin if any threat is detected with the help of a communication module. Keywords: Deep learning, Raspberry Pi, OpenCV, Image Processing, YOLO, Face Recognition

Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras

2020 ◽  
Vol 27 (4) ◽  
pp. 373-387 ◽  
Author(s):  
Jesus Benito-Picazo ◽  
Enrique Domínguez ◽  
Esteban J. Palomo ◽  
Ezequiel López-Rubio
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Low Cost ◽  
Abnormal Behavior ◽  
Video Frame ◽  
Raspberry Pi ◽  
Video Surveillance System ◽  
Surveillance Systems

The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented on low cost devices, due to the excessive computational load generated by the examination of multiple image windows. This work presents a video surveillance system aimed to detect moving objects with abnormal behavior for a panoramic 360∘ surveillance camera. The block of the video frame to be analyzed is determined on the basis of a probabilistic mixture distribution comprised by two mixture components. The first component is a uniform distribution, which is in charge of a blind window selection, while the second component is a mixture of kernel distributions. The kernel distributions generate windows within the video frame in the vicinity of the areas where anomalies were previously found. This contributes to obtain candidate windows for analysis which are close to the most relevant regions of the video frame, according to the past recorded activity. A Raspberry Pi microcontroller based board is employed to implement the system. This enables the design and implementation of a system with a low cost, which is nevertheless capable of performing the video analysis with a high video frame processing rate.

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.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Deep Neural Network for Better Face Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 2236-2239
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Detection ◽  
Deep Neural Network ◽  
Low Cost ◽  
Raspberry Pi ◽  
The Face ◽  
Frame Work ◽  
Haar Cascade ◽  
Cascade Method

This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.

scholarly journals Raspberry Pi Based Home Surveillance System using SMTP

2020 ◽  
Vol 9 (1) ◽  
pp. 2792-2794
Keyword(s):  
Computer Vision ◽  
Surveillance System ◽  
High Efficiency ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Processing Unit ◽  
Central Processing ◽  
Local Database ◽  
Python Language ◽  
Transfer Protocol

Different Technologies are emerging in the field of Home Surveillance now a days. Surveillance systems are being used to reduce man power and to increase security of a home. Technologies like Computer Vision and Internet of Things (IOT) are one of them. In this project a surveillance system has been implemented employing a single board computer i.e. Raspberry Pi 3 which will act like a central processing unit with the help of python language and a module named as Open Source Computer Vision(Open CV).To make it more automated a local database of authorized persons has been made. It will store the images of the different authorized persons who can enter in that security area. Camera will be always in surveillance mode and it will be searching for a face persistently. It’ll act as Computer Vision. This will lead to more accurate system with high efficiency. Therefore it’ll capture the image of the person automatically and compare it with the local database. In the case of match, door will be open automatically otherwise in the case of unauthorized person, system will send the image of the unauthorized person to owner of the home via SMTP(Simple Mail Transfer Protocol). A local library in Python - "smtplib" is being press into service to send messages. The smtplib module characterizes a SMTP customer meeting object that can be utilized to send messages to any Web machine with SMTP( Simple Mail Transfer Protocol). Also a webpage has been made with the help of apache server to store the images of unauthorized persons.

scholarly journals A NOVEL DEEP LEARNING BASED METHOD FOR DETECTION AND COUNTING OF VEHICLES IN URBAN TRAFFIC SURVEILLANCE SYSTEMS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 793-800
Author(s):  
J. J. Majin ◽  
Y. M. Valencia ◽  
M. E. Stivanello ◽  
M. R. Stemmer ◽  
J. D. Salazar
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Management Strategies ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Surveillance Systems ◽  
Real Time Processing ◽  
Detection Model

Abstract. In intelligent transportation systems (ITS), it is essential to obtain reliable statistics of the vehicular flow in order to create urban traffic management strategies. These systems have benefited from the increase in computational resources and the improvement of image processing methods, especially in object detection based on deep learning. This paper proposes a method for vehicle counting composed of three stages: object detection, tracking and trajectory processing. In order to select the detection model with the best trade-off between accuracy and speed, the following one-stage detection models were compared: SSD512, CenterNet, Efficiedet-D0 and YOLO family models (v2, v3 and v4). Experimental results conducted on the benchmark dataset show that the best rates among the detection models were obtained using YOLOv4 with mAP = 87% and a processing speed of 18 FPS. On the other hand, the accuracy obtained in the proposed counting method was 94% with a real-time processing rate lower than 1.9.

scholarly journals Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

2021 ◽  
Vol 15 (23) ◽  
pp. 104-119
Author(s):  
Ervan Adiwijaya Haryadi ◽  
Grafika Jati ◽  
Ario Yudo Husodo ◽  
Wisnu Jatmiko
Keyword(s):  
Surveillance System ◽  
Low Cost ◽  
Face Mask ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Single Shot ◽  
Surveillance Camera ◽  
Smart Surveillance ◽  
The Face ◽  
Smart Surveillance System

A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic.  Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.

scholarly journals Smart Automated Surveillance System using Raspberry Pi

2020 ◽  
Vol 9 (2) ◽  
pp. 308-311
Keyword(s):  
Surveillance System ◽  
Human Life ◽  
Security System ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Memory Consumption ◽  
Security Systems ◽  
Confined Spaces ◽  
Automated Surveillance ◽  
Use Of The Internet

With the fast-growing world, frequent attacks and burglaries are increased. Therefore, the need for an effective and reliable surveillance security system has become an indispensable necessity to fulfill various security aspects and add quality to human life. The existing security systems use CCTV cameras and computers. It also consumes a lot of memory because of continuous recording and needed manpower to detect unauthorized activities and instant notification is also not possible in these surveillance security systems. So we researched the surveillance part and mainly on the burglary part where during the absence of the owner the camera will detect motion and will send instant notification to the user when motion is detected. Compared to existing surveillance systems, the use of Raspberry pi is effective because of its size, low power, and memory consumption, wireless features, and many more effective aspects. In this paper, we proposed an IoT based surveillance security system that can be accessed remotely with the use of the internet. This framework can be used in homes and personal offices. The framework works best in confined spaces and when the space in which it is being used has the absence of the owner. This is because the system will detect any movement occurring in the space.

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