Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning.pdf

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Building Occupancy Detection and
Localization Using CCTV Camera and
Deep Learning
Abstract
Occupancy information plays a key role in analyzing and improving building
energy performance. The advances of Internet of Things (IoT) technologies
have engendered a shift in measuring building occupancy with IoT sensors, in
which cameras in closed-circui
measurements. However, existing camera
approaches cannot function well when scanning videos with a number of
occupants and determining occupants’ locations. This article aims to devel
a novel deep-learning-based approach for better building occupancy detection
based on CCTV cameras. To do so, this research proposes a deep
model to detect the number of occupants and determine their locations in
videos. This model consists of
and three-stage occupancy detection. The first module presents a deep
convolutional neural network to perform residual and multibranch
Building Occupancy Detection and
Localization Using CCTV Camera and
Deep Learning
Occupancy information plays a key role in analyzing and improving building
energy performance. The advances of Internet of Things (IoT) technologies
have engendered a shift in measuring building occupancy with IoT sensors, in
circuit television (CCTV) systems can provide richer
measurements. However, existing camera-based occupancy detection
approaches cannot function well when scanning videos with a number of
occupants and determining occupants’ locations. This article aims to devel
based approach for better building occupancy detection
based on CCTV cameras. To do so, this research proposes a deep
model to detect the number of occupants and determine their locations in
videos. This model consists of two main modules, namely, feature extraction
stage occupancy detection. The first module presents a deep
convolutional neural network to perform residual and multibranch
Building Occupancy Detection and
Localization Using CCTV Camera and
Occupancy information plays a key role in analyzing and improving building
energy performance. The advances of Internet of Things (IoT) technologies
have engendered a shift in measuring building occupancy with IoT sensors, in
t television (CCTV) systems can provide richer
based occupancy detection
approaches cannot function well when scanning videos with a number of
occupants and determining occupants’ locations. This article aims to develop
based approach for better building occupancy detection
based on CCTV cameras. To do so, this research proposes a deep-learning
model to detect the number of occupants and determine their locations in
two main modules, namely, feature extraction
stage occupancy detection. The first module presents a deep
convolutional neural network to perform residual and multibranch

convolutional calculation to extract shallow and semantic features, and
constructs feature pyramids through a bidirectional feature network. The
second module performs a three-stage detection procedure with three
sequential and homogeneous detectors which have increasing Intersection
over Union (IoU) thresholds. Empirical experiments evaluate the detection
performance of the approach with CCTV videos from a university building.
Experimental results show that the approach achieves the superior detection
performance when compared with baseline models.

This document presents a study on object detection using SSD-MobileNet. The researchers developed a lightweight object detection model using SSD-MobileNet that can perform real-time object detection on embedded systems with limited processing resources. They tested the model on images and video captured using webcams. The model was able to detect objects like people, cars, and animals with good accuracy. The SSD-MobileNet framework provides fast and efficient object detection for applications like autonomous driving assistance systems that require real-time performance on low-power devices.

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In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.

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In this paper, we propose a system that combines real time object detection using the YOLOv3 algorithm with audio feedback to assist visually impaired individuals in locating and identifying objects in their surroundings. The YOLOv3 algorithm is a state of the art object detection algorithm that has been used in numerous studies for various applications. Audio feedback has also been studied in previous research as a useful tool for assisting visually impaired individuals. Our proposed system builds on the effectiveness of both these technologies to provide a valuable tool for improving the independence and quality of life of visually impaired individuals. We present the architecture of our proposed system, which includes a YOLOv3 model for object detection and a text to speech engine for providing audio feedback. We also present the results of our experiments, which demonstrate the effectiveness of our system in detecting and identifying objects in real time. Our proposed system can be used in various settings, such as indoor and outdoor environments, and can assist visually impaired individuals in various activities such as the navigation and object identification. Dr. K. Nagi Reddy | K. Sreeja | M. Sreenivasulu Reddy | K. Sireesha | M. Triveni "Real Time Object Detection with Audio Feedback using Yolo_v3" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55158.pdf Paper URL: https://www.ijtsrd.com.com/engineering/electronics-and-communication-engineering/55158/real-time-object-detection-with-audio-feedback-using-yolov3/dr-k-nagi-reddy

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Threshold based filtering technique for efficient moving object detection and...

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Abstract Detection and tracking of moving objects are an important research area in a video surveillance application. Object tracking is used in several applications such as video compression, surveillance, robot technology and so on. Recently many researches has been developed for video object detection, however the object detection accuracy and background object detection in the video frames are still poses demanding issues. In this paper, a novel framework called Threshold Filtered Video Object Detection and Tracking (TFVODT) is designed for effective detection and tracking of moving objects. TFVODT framework initially takes video file as input, and then video frames are segmented using Median Filter-based Enhanced Laplacian Thresholding for improving the video quality by reducing mean square error. Next, Color Histogram-based Particle Filter is applied to the segmented objects in TFVODT framework for video object tracking. The Color Histogram-based Particle Filter measures the likelihood function, particle posterior and particle prior function based on the Bayes Sequential Estimation model for improving the object tracking accuracy. Finally, the objects detection is performed with help of Improvisation of Enhanced Laplacian Threshold (IELT) to enhance video object detection accuracy and to recognize background moving object detection. The proposed TFVODT framework using video images obtained from Internet Archive 501(c) (3) for conducting experiment and comparison is made with the existing object detection techniques. Experimental evaluation of TFVODT framework is done with the performance metrics such as object segmentation accuracy, Peak Signal to Noise Ratio, object tracking accuracy, Mean Square Error and object detection accuracy of moving video object frames. Experimental analysis shows that the TFVODT framework is able to improve the video object detection accuracy by 18% and reduces the Peak Signal to Noise Ratio by 23 % when compared to the state-ofthe- art works. Keywords: Object segmentation, Object tracking, Object Detection, Enhanced Laplacian Thresholding, Median Filter, Color Histogram-based Particle Filter

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This document describes a project to develop a real-time object detection system using convolutional neural networks (CNNs) implemented in MATLAB. The system will leverage deep learning techniques like CNNs to accurately detect and localize objects in real-time video streams. The key objectives are to achieve state-of-the-art accuracy and reliability in object detection with minimal false positives/negatives, while also enabling real-time performance by processing video frames swiftly. The document provides background on CNNs and object detection, and outlines the proposed system architecture which includes collecting an annotated dataset, preprocessing the data, training a CNN model, and evaluating model performance on a test set.

This document proposes and evaluates several deep learning models for unsupervised monocular depth estimation. It begins with background on depth estimation methods and a literature review of recent work. Four depth estimation architectures are then described: EfficientNet-B7, EfficientNet-B3, DenseNet121, and DenseNet161. These models use an encoder-decoder structure with skip connections. An unsupervised loss function is adopted that combines appearance matching, disparity smoothness, and left-right consistency losses. The models are trained on the KITTI dataset and evaluated using standard KITTI metrics, showing improved performance over baseline methods using less training data and lower input resolution.

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This document provides a summary of a report on 3D object recognition using the Point Cloud Library (PCL). It describes the key steps in the global and local pipelines for 3D object recognition. The global pipeline is used for object detection and classification, while the local pipeline determines the object's pose. The report details the training and testing process for each pipeline, including keypoint detection, descriptor calculation, and matching. It also presents results from experiments on public and custom datasets and analyzes the performance of different algorithm combinations.

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Exploring the Latest Innovations in CCTV Surveillance Solutions.pdf

Trustly Technology Solutions Pvt. Ltd.

Surveillance systems play a crucial role in ensuring security and maintaining peace of mind in today's rapidly changing technological landscape. For decades, closed-circuit television (CCTV) surveillance solutions have dominated this field. However, CCTV systems have undergone significant transformations as a result of advancements in cloud computing, artificial intelligence (AI), and data analytics.

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People Monitoring and Mask Detection using Real-time video analyzing

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People Counting and mask detection based on video is an important field in a Computer Vision. There is growing interest in video-based solutions for people monitoring and counting in business and security applications using Computer Vision technology. It has been effectively used in many Artificial Intelligence fields. Compareing to normal sensor based solutions the one with video based allows more flexible performance, improved functionalities with lower costs. The system with people counter program requires more processing because that deals with real-time video, so this particular proposed technique converts a color image into binary in order to minimize data of image. Reducing processing time is an important term in Software Engineering to build a good working system. People counting methods based on head detection and tracking to evaluate the total number of people who move under an overhead camera and check whether that people are wearing a mask or not. There basically four main features in this proposed system: People counting, Mask detection, Alarm alert and Scan ID. Based on tracking of head, this method uses the crossing-line judgment to determine whether the particular head object will get counted or not to be counted. The two main challenges overcome in this system are: tough estimation of the background scene and the number of persons in merge split scenarios. A technique for masked face detection using three different steps of estimating eye line detection, facial part detection and eye detection is used in this system. On exceeding the count of people or in case mask is not worn then alarm gets alerted

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The document proposes a smart indoor localization system using deep learning and a software engineering model. It consists of two parts: a software part for collecting and processing image data to train a CNN model, and a hardware part using a Raspberry Pi camera on a robotic car to capture real-time images for localization. The software part trains a CNN to 99.6% accuracy on the training dataset and 100% on the validation dataset to classify images into different indoor locations. A simple website is designed to control the hardware part and interface with the trained model for indoor localization predictions.

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Review of Pose Recognition Systems

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Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.

Q180305116119

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Machine learning based augmented reality for improved learning application th...

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Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.

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Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning.pdf

1. Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning Abstract Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in closed-circui measurements. However, existing camera approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to devel a novel deep-learning-based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep model to detect the number of occupants and determine their locations in videos. This model consists of and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in circuit television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to devel based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep model to detect the number of occupants and determine their locations in videos. This model consists of two main modules, namely, feature extraction stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch Building Occupancy Detection and Localization Using CCTV Camera and Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in t television (CCTV) systems can provide richer based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to develop based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep-learning model to detect the number of occupants and determine their locations in two main modules, namely, feature extraction stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch

2. convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bidirectional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves the superior detection performance when compared with baseline models.

Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning.pdf

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Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning.pdf