Object Tracking Technology Trends, Challenges and Applications /
With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In...
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| Andere auteurs: | , , , |
| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
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| Editie: | 1st ed. 2023. |
| Reeks: | Contributions to Environmental Sciences & Innovative Business Technology,
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| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/978-981-99-3288-7 |
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| 245 | 1 | 0 | |a Object Tracking Technology |h [electronic resource] : |b Trends, Challenges and Applications / |c edited by Ashish Kumar, Rachna Jain, Ajantha Devi Vairamani, Anand Nayyar. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XIV, 274 p. 104 illus.). |b 83 illus. in color. | ||
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| 490 | 1 | |a Contributions to Environmental Sciences & Innovative Business Technology, |x 2731-8311 | |
| 505 | 0 | |a Single Object Detection from Video Streaming -- Different Approaches to Background Subtraction and Object Tracking in Video Streams: A Review -- Auto Alignment of Tanker Loading Arm Utilizing Stereo-Vision Video and 3D Euclidean Scene Reconstruction -- Visual Object Segmentation Improvement using Deep Convolutional Neural Networks -- Applications of Deep Learning based Methods on Surveillance Video Stream by Tracking Various Suspicious Activities -- Hardware Design Aspects of Visual Tracking System -- Automatic Helmet (Object) Detection and Tracking the Riders using Kalman Filter Technique -- Deep Learning based Multi-Object Tracking -- Multiple Object Tracking of Autonomous Vehicles for Sustainable and Smart Cities -- Multi Object Detection: A Social Distancing Monitoring System -- Investigating Two Stage Detection Methods Using Traffic Light Detection Dataset. | |
| 520 | |a With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Image processing. | |
| 650 | 0 | |a Image processing |x Digital techniques. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 1 | 4 | |a Image Processing. |
| 650 | 2 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Kumar, Ashish. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Jain, Rachna. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Vairamani, Ajantha Devi. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Nayyar, Anand. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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