The present work deals with real-time database updation of moving vehicle through dynamic automation system, where position and internal conditions of the car are auto-saved in the cloud server. The total embedded system is a simulated prototype, already implemented virtually for smaller distance movement on suitable road condition, where the auto-checking of the fuel remained shows the novelty of the system compared to the existing data available in literature. As evident form experimental results, soil moistures sensor detects the road condition, whereas ultrasonic helps to reduce the accident probability with real-time tracking. Humidity and temperature sensors also give real-time environmental condition outside the car which is crucial while driving at extreme climatic condition. Insurance companies as beneficiaries also check the status of it if the car is suffered from accidents or unnecessary external coercive influences. Data display facility is available at real time to avoid any unwanted proximity with nearby moving/standing objects, and later can be retrieved from the secured database. The work may be extended as a complete autonomous tracker with condition monitor in future automobile industry. 

Dynamic Automation of a Moving Vehicle at Real time along with Position and Condition Tracking

The automatic recognition for Internet of Things (IoT) supervision images is a prerequisite for the detection of abnormalities in monitoring images. This technology is a developmental trend in video surveillance. Various video image detection and recognition methods have certain weaknesses, such as poor generalization ability and poor anti-interference ability. In response, this paper conducts a study on automatic recognition for IoT supervision images based on modal decomposition. The paper presents an overall framework of the IoT supervision system. For the problems of poor real-time performance and few samples that commonly exist in video stream target recognition, the paper proposes a dynamic modal decomposition-based feature extraction algorithm for IoT supervision video stream to build a suitable platform for IoT supervision image foreground segmentation. The paper selects a dictionary with rich elements and exchanges a high computation time for minimizing the reconstruction error generated by applying the dynamic modal decomposition method. Experimental results validate the effectiveness of the proposed algorithm.

Automatic Recognition for IoT Supervision Images Based on Modal Decomposition

In recent decades, object detection has played animportant role in real-time applications. The threat of accidents and criminality was mounting, and the population was growing quickly as a result, covering technology is improving on a daily basis classifying items is a simple activity for humans but it is a difficult assignment for robots owing to the variety of elements, where colourful models have been used to break up this sensitive process. There are several methods for depicting multiple things this study examines several models that have been preliminarily imposed to describe numerous items in real time. This becomes a challenging task as it takes a long time to reuse an accurate object recognition. Hence, developing a novel architecture to perform genuine object detection is highly required. This study provides a method for detecting numerous items and discusses about different machine learning models, including R-CNN, SSD, and YOLO. It also indicates that several designs were utilised to build A frame for holding generally realistic models for detection, recognition, and identification.

A Novel Survey on ML based Vehicle Detection for Dynamic Traffic Control

In recent decades, object detection has played animportant role in real-time applications. The threat of accidents and criminality was mounting, and the population was growing quickly as a result, covering technology is improving on a daily basis classifying items is a simple activity for humans but it is a difficult assignment for robots owing to the variety of elements, where colourful models have been used to break up this sensitive process. There are several methods for depicting multiple things this study examines several models that have been preliminarily imposed to describe numerous items in real time. This becomes a challenging task as it takes a long time to reuse an accurate object recognition. Hence, developing a novel architecture to perform genuine object detection is highly required. This study provides a method for detecting numerous items and discusses about different machine learning models, including R-CNN, SSD, and YOLO. It also indicates that several designs were utilised to build A frame for holding generally realistic models for detection, recognition, and identification. 

The Aspect Extraction Using Topic-Aware Dynamic Convolutional Neural Network

The discipline of computer vision is becoming more popular as a research subject. In a surveillance-based computer vision application, item identification and tracking are the core procedures. They consist of segmenting and tracking an object of interest from a sequence of video frames, and they are both performed using computer vision algorithms. In situations when the camera is fixed and the backdrop remains constant, it is possible to detect items in the background using more straightforward methods. Aerial surveillance, on the other hand, is characterized by the fact that the target, as well as the background and video camera, are all constantly moving. It is feasible to recognize targets in the video data captured by an unmanned aerial vehicle (UAV) using the mean shift tracking technique in combination with a deep convolutional neural network (DCNN). It is critical that the target detection algorithm maintains its accuracy even in the presence of changing lighting conditions, dynamic clutter, and changes in the scene environment. Even though there are several approaches for identifying moving objects in the video, background reduction is the one that is most often used. An adaptive background model is used to create a mean shift tracking technique, which is shown and implemented in this work. In this situation, the background model is provided and updated frame-by-frame, and therefore, the problem of occlusion is fully eliminated from the equation. The target tracking algorithm is fed the same video stream that was used for the target identification algorithm to work with. In MATLAB, the works are simulated, and their performance is evaluated using image-based and video-based metrics to establish how well they operate in the real world.

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Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

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Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance