Cite this DOI

Dr Subba Reddy Borra, D. S. R. B. (2023). Deep CNN Framework for Object Detection and Classification System from Real Time Videos. *Journal of Science & Technology*, *8*(12), 78–93. https://doi.org/10.46243/jst.2023.v8.i12.pp78-93

@article{drsubbareddyborra2023deep,
  author    = {Dr Subba Reddy Borra, Dr Subba Reddy Borra},
  title     = {{Deep CNN Framework for Object Detection and Classification System from Real Time Videos}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {dec},
  volume    = {8},
  number    = {12},
  pages     = {78--93},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i12.pp78-93},
  url       = {https://doi.org/10.46243/jst.2023.v8.i12.pp78-93},
  language  = {en},
  abstract  = {Convolutional Neural Network (CNN) framework, a type of artificial intelligence architecture specifically designed to excel at visual tasks.The primary objective of this system is twofold: firstly, to accurately detect objects within a given video stream, and secondly, to classify these detected objects into predefined 1Professor,2UG Students, Department of Information Technology 1,2Malla Reddy Engineering College for Women, Maisammaguda, Dhulapally, Kompally, Secunderabad-500100, Telangana, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp78 -93 47 Dr Subba Reddy Borra, B Gayatri, B Rekha, B Akshitha: Deep CNN Framework for Object Detection and Classification System from Real Time Videos categories. The real-time nature of the videos adds an extra layer of complexity, requiring the system to process and analyze frames swiftly and efficiently. In simpler terms, imagine a smart system that can automatically identify and categorize various objects appearing in videos as they unfold in real-time. This could find applications in diverse fields such as surveillance, autonomous vehicles, or even in enhancing user experiences in entertainment and gaming.The cornerstone of this technology lies in the deep learning capabilities of the CNN framework. Deep learning enables the system to learn intricate patterns and features from vast amounts of data, allowing it to recognize and differentiate objects with a high degree of accuracy.By combining the power of deep learning with the real-time processing of videos, this framework promises to open new avenues for applications where swift and precise object detection and classification are crucial. The ensuing sections will delve into the technical aspects, methodologies, and potential applications of this advanced system, shedding light on its significance in the evolving landscape of computer vision and artificial intelligence. The impetus for undertaking research in the development of a Deep Convolutional Neural Network (CNN) framework for real-time object detection and classification in videos stems from the pressing need for advanced and efficient computer vision systems. In contemporary scenarios, there is an escalating demand for intelligent systems capable of swiftly analyzing and comprehending dynamic visual information, especially in domains like surveillance, autonomous systems, and interactive media. Traditional computer vision methods have exhibited limitations in handling the complexity of real-time video streams with a multitude of objects. The motivation arises from the recognition that a more sophisticated approach, rooted in deep learning, is required to surmount these challenges effectively. Real-time video analysis demands not only accurate detection of objects within frames but also rapid classification, a task that necessitates a high level of computational efficiency. The proposed CNN framework aims to address these requisites by leveraging the hierarchical and discriminative features learned through deep neural networks.The growing ubiquity of video data in various applications intensifies the need for systems that can discern and interpret visual content with precision. Applications such as video surveillance require swift and reliable object detection to ensure timely responses to potential threats. Similarly, in autonomous vehicles, the ability to rapidly identify and classify objects in the vehicle’s vicinity is imperative for safe navigation. This research is motivated by the conviction that a well-designed CNN framework, adept at handling the intricacies of real-time video analysis, can significantly enhance the capabilities of computer vision systems. The overarching goal is to contribute to the development of intelligent systems that can operate seamlessly in dynamic environments, catering to the increasing demands for accuracy and speed in object detection and classification within real-time video streams. LITERATURE SURVEY Image classification, as a classical research topic in recent years, is one of the core issues of computer vision and the basis of various fields of visual recognition. The improvement of classification network performance tends to significantly improve its application level, for example to object-detection, segmentation, human pose estimation, video classification, object tracking, and super-resolution technology. Improving image classification technology is an important part of promoting the development of computer vision. Its main process includes image data preprocessing, feature extraction and representation, and classifier design. The focus of image classification research has always been image feature extraction, which is the basis of image classification. Traditional image feature extraction algorithms focus more on manually setting specific image features In the last decade, modern video surveillance systems have attracted i}
}

TY  - JOUR
TI  - Deep CNN Framework for Object Detection and Classification System from Real Time Videos
AU  - Dr Subba Reddy Borra, Dr Subba Reddy Borra
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/12/12/
VL  - 8
IS  - 12
SP  - 78
EP  - 93
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Convolutional Neural Network (CNN) framework, a type of artificial intelligence architecture specifically designed to excel at visual tasks.The primary objective of this system is twofold: firstly, to accurately detect objects within a given video stream, and secondly, to classify these detected objects into predefined 1Professor,2UG Students, Department of Information Technology 1,2Malla Reddy Engineering College for Women, Maisammaguda, Dhulapally, Kompally, Secunderabad-500100, Telangana, India. DOI:https://doi.org/10.46243/jst.2023.v8.i12.pp78 -93 47 Dr Subba Reddy Borra, B Gayatri, B Rekha, B Akshitha: Deep CNN Framework for Object Detection and Classification System from Real Time Videos categories. The real-time nature of the videos adds an extra layer of complexity, requiring the system to process and analyze frames swiftly and efficiently. In simpler terms, imagine a smart system that can automatically identify and categorize various objects appearing in videos as they unfold in real-time. This could find applications in diverse fields such as surveillance, autonomous vehicles, or even in enhancing user experiences in entertainment and gaming.The cornerstone of this technology lies in the deep learning capabilities of the CNN framework. Deep learning enables the system to learn intricate patterns and features from vast amounts of data, allowing it to recognize and differentiate objects with a high degree of accuracy.By combining the power of deep learning with the real-time processing of videos, this framework promises to open new avenues for applications where swift and precise object detection and classification are crucial. The ensuing sections will delve into the technical aspects, methodologies, and potential applications of this advanced system, shedding light on its significance in the evolving landscape of computer vision and artificial intelligence. The impetus for undertaking research in the development of a Deep Convolutional Neural Network (CNN) framework for real-time object detection and classification in videos stems from the pressing need for advanced and efficient computer vision systems. In contemporary scenarios, there is an escalating demand for intelligent systems capable of swiftly analyzing and comprehending dynamic visual information, especially in domains like surveillance, autonomous systems, and interactive media. Traditional computer vision methods have exhibited limitations in handling the complexity of real-time video streams with a multitude of objects. The motivation arises from the recognition that a more sophisticated approach, rooted in deep learning, is required to surmount these challenges effectively. Real-time video analysis demands not only accurate detection of objects within frames but also rapid classification, a task that necessitates a high level of computational efficiency. The proposed CNN framework aims to address these requisites by leveraging the hierarchical and discriminative features learned through deep neural networks.The growing ubiquity of video data in various applications intensifies the need for systems that can discern and interpret visual content with precision. Applications such as video surveillance require swift and reliable object detection to ensure timely responses to potential threats. Similarly, in autonomous vehicles, the ability to rapidly identify and classify objects in the vehicle’s vicinity is imperative for safe navigation. This research is motivated by the conviction that a well-designed CNN framework, adept at handling the intricacies of real-time video analysis, can significantly enhance the capabilities of computer vision systems. The overarching goal is to contribute to the development of intelligent systems that can operate seamlessly in dynamic environments, catering to the increasing demands for accuracy and speed in object detection and classification within real-time video streams. LITERATURE SURVEY Image classification, as a classical research topic in recent years, is one of the core issues of computer vision and the basis of various fields of visual recognition. The improvement of classification network performance tends to significantly improve its application level, for example to object-detection, segmentation, human pose estimation, video classification, object tracking, and super-resolution technology. Improving image classification technology is an important part of promoting the development of computer vision. Its main process includes image data preprocessing, feature extraction and representation, and classifier design. The focus of image classification research has always been image feature extraction, which is the basis of image classification. Traditional image feature extraction algorithms focus more on manually setting specific image features In the last decade, modern video surveillance systems have attracted i
DO  - 10.46243/jst.2023.v8.i12.pp78-93
UR  - https://doi.org/10.46243/jst.2023.v8.i12.pp78-93
ER  -

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