A Survey Paper on Moving Object Detection Using Deep Learning (original) (raw)
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Object Detection using Deep Learning Approach
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The most often utilized strategies for current deep learning models to accomplish a multitude of activities on devices are mobile networks and binary neural networks. In this research, we propose a method for identifying an object using the pretrained deep learning model MobileNet for Single Shot Multi-Box Detector (SSD). This technique is utilized for real-time detection as well as webcams to detect the object in a video feed.To construct the module, we use the MobileNet and SSD frameworks to provide a faster and effective deep learning-based object detection approach.Deep learning has evolved into a powerful machine learning technology that incorporates multiple layers of features or representations of data to get cuttingedge results. Deep learning has demonstrated outstanding performance in a variety of fields, including picture classification, segmentation, and object detection. Deep learning approaches have recently made significant progress in fine-grained picture categorization, which tries to differentiate subordinate-level categories.The major goal of our study is to investigate the accuracy of an object identification method called SSD, as well as the significance of a pre-trained deep learning model called MobileNet.To perform this challenge of detecting an item in an image or video, I used OpenCV libraries, Python, and NumPy. This enhances the accuracy of behavior recognition at a processing speed required for real-time detection and daily monitoring requirements indoors and outdoors.
An Overview of Deep Learning-Based Object Detection Methods
2018
In recent years, there has been rapid development in the research area of deep learning. Deep learning was used to solve different problems, such as visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems. In this paper we'll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, since the creation of AlexNet in 2012. Then, we'll make a comparison in terms of speed and accuracy between the most used state-of-the-art methods in object detection. Keywords— Object Detection, Deep Learning Methods, Convolutional Neural Networks
A Survey of Deep Learning-based Object Detection
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.
Real Time Object Detection and Tracking using Deep Learning
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Efficient Object Recognition and Tracking are main challenging assignments in computer vision techniques. A very big challenge in many object detection techniques using deep learning may lead to slow and non-accurate performance. This Project Aims to detect and tracking of objects efficiently and accurately in real time .Detecting any object is important in understanding object activities. Here we completely used deep learning networking techniques .The network is trained on most used and challenging dataset COCO. The result is very fast and accurate where object recognition is required.
Applied Sciences, 2020
In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian...
Object Detection Techniques based on Deep Learning: A Review
Computer Science & Engineering: An International Journal, 2022
Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental compo...
Deep Learning Approaches for Object Detection
2020
In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.