5/7/2023 0 Comments Wolfram player capanilities![]() Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection. This changed with the rise of powerful deep networks. Tracking has traditionally been the art of following interest points through space and time. The article concludes with identifying the requirements and discuss open research challenges in Edge computing. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. ![]() At last, conclude by identifying promising future directions. Detailed discussions on some important applications in object detection areas such as pedestrian detection, crowd detection, etc, and real-time object detection on Gpu-based embedded systems have been presented. ![]() 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. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques is presented. Earlier traditional detection methods were used for detecting the objects since 2012 with the introduction of convolutional neural networks thereafter deep learning-based techniques were used for feature extraction and it led to remarkable breakthroughs in this area. ![]() One of the most challenging and fundamental problem in object detection is locating a specific object from the multiple-objects present in the scene. In recent years there is remarkable progress in one of the computer vision application area is object detection. ![]()
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