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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | IoT Based Smart Object Detector: A Survey |
| Authors Name: | Sharik Sameera Maliksab Mulla , Anjana P. Umarji , Aishwarya B. Sangapur , Saniha Hunur , Parashuram K. Deshpande |
| Unique Id: | IJSDR2303011 |
| Published In: | Volume 8 Issue 3, March-2023 |
| Abstract: | The survey addresses how an IOT smart device helps Object detection, tracking, and recognition recently have been amongst the most interesting topics for research in computer vision and its application. Object tracking is a computer vision technique used to locate and follow objects of interest in a video stream. The goal of object tracking is to track the movement of the object through the frames of the video, even if the object undergoes changes in size, orientation, or illumination. Object tracking has applications in various fields, including security surveillance, traffic monitoring, sports analysis, and robotics. Object trackers use a variety of techniques, including feature-based tracking, appearance-based tracking, and motion-based tracking. Feature-based tracking involves detecting key features of the object and using them to track its movement. Appearance-based tracking involves creating a model of the object's appearance and using it to identify the object in subsequent frames. Motion-based tracking involves using the object's motion information to track its movement. There are several challenges in object tracking, including occlusion, motion blur, and changes in lighting conditions. To address these challenges, object trackers often use a combination of techniques and algorithms, including filtering, prediction, and data association. Deep learning-based approaches, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have also shown promising results in object tracking. This survey may help to track and monitor the objects. In this paper initially the introduction is given, next to it the elaboration of some papers related to the topic are explained and so on followed by the conclusion. |
| Keywords: | Object Tracking, Object Detection, Motion blur, Security surveillance |
| Cite Article: | "IoT Based Smart Object Detector: A Survey", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 3, page no.50 - 57, March-2023, Available :http://www.ijsdr.org/papers/IJSDR2303011.pdf |
| Downloads: | 00032 |
| Publication Details: | Published Paper ID: IJSDR2303011 Registration ID:204278 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: 50 - 57 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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