Multi-modal cyber security based object detection by classification using deep learning and background suppression techniques

Srinivas, Kalyanapu; Singh, Laxman; Chavva, Subba Reddy; Dappuri, Bhasker; Chandrasekaran, Saravanan; Qamar, Shamimul

doi:10.1016/j.compeleceng.2022.108333

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…The performance validation reveals precision rates of more than 90%, accuracy values greater than 70%, and recall levels greater than 70%, verifying its intended functionality [17]. Srinivas et al (2022) examined frameworks for these activities using better cyber security control facilities. The technique is divided into two stages: detection of numerous objects using the cyber security probabilistic Gaussian mixture model and background suppression, and tracking of multiple moving objects using the kernel convolution moving window with kalman filter.…”

Section: Introductionmentioning

confidence: 85%

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Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Mohandoss

2023

JMC

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Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.

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Section: Introductionmentioning

confidence: 85%

“…Simulations outcomes show that the suggested approach can identify and locate objects in complicated and shifting environments with excellent efficiency, resilience, and accuracy. This proposed model also yields a noise-free image [18].…”

Section: Introductionmentioning

confidence: 98%

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Mohandoss

2023

JMC

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“…It aids in detecting and tracking people, vehicles, and other objects of interest, alerting security personnel to potential threats or unusual activities. This capability enhances the efficiency and effectiveness of security operations [10].…”

Section: ) Surveillance and Securitymentioning

confidence: 99%

Progress in Object Detection: An In-Depth Analysis of Methods and Use Cases

Tasnim,

2023

EJECE

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Object detection, a fundamental task in computer vision, involves identifying and localizing objects within images or videos. This paper provides a comprehensive review of traditional and deep learning-based object detection techniques and their applications, challenges, and future directions. We first discuss traditional object detection methods, which rely on handcrafted features and classical machine learning algorithms. We then explore the advancements brought by deep learning, including convolutional neural networks (CNNs) and transformer-based architectures, which have significantly improved the accuracy and efficiency of object detection tasks. A thorough comparison and evaluation of different object detection techniques are presented, considering performance metrics, speed, and robustness to object size, orientation, and occlusion variations. We also examine the diverse applications of object detection across various domains, such as robotics, autonomous vehicles, surveillance, medical imaging, and augmented reality. We outline open challenges and future research directions, emphasizing the need to combine object detection with other tasks, develop few-shot and zero-shot learning approaches, and address issues related to fairness, accountability, and transparency. This paper aims to comprehensively review the most prominent object detection techniques, their evolution, and their applications in diverse domains. We discussed traditional methods and recent deep learning-based approaches, emphasizing their strengths and limitations.

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“…The object, for instance, is like people, vehicles, faces, pedestrians, etc in an image or video. It is a challenging task when the object is under poor illumination (direct sunlight, insufficient light), suddenly changes the environment, and variation in the targeted object (Srinivas et al, 2022).…”

Section: Object Detection and Processing Techniquementioning

confidence: 99%

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

Kumar

Sharma

Kumar

2023

Int. j. math. eng. manag. sci.

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Pedestrian safety has become a critical consideration in developing society especially road traffic, an intelligent transportation need of the hour is the solution left. India tops the world with 11% of global road accidents. With this data, we have moved in the direction of computer vision applications for efficient and accurate pedestrian detection for intelligent transportation systems (ITS). The important application of this research is robot development, traffic management and control, unmanned vehicle driving (UVD), intelligent monitoring and surveillance system, and automatic pedestrian detection system. Much research has focused on pedestrian detection, but sustainable solution-driven research must still be required to overcome road accidents. We have proposed a wireless sensor network-based pedestrian detection system that classifies the real-time set of pedestrian activity and samples the reciprocally received signal strength (RSS) from the sensor node. We applied a histogram of oriented gradient (HOG) descriptor algorithm K-nearest neighbor, decision tree and linear support vector machine to measure the performance and prediction of the target. Also, these algorithms have performed a comparative analysis under different aspects. The linear support vector machine algorithm was trained with 481 samples. The performance achieves the accuracy of 98.90%and has accomplished superior results with a maximum precision of 0.99, recall of 0.98, and F-score of 0.95 with 2% error rate. The model’s prediction indicates that it can be used in the intelligent transportation system. Finally, the limitation and the challenges discussed to provide an outlook for future research direction to perform effective pedestrian detection.

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Multi-modal cyber security based object detection by classification using deep learning and background suppression techniques

Cited by 11 publications

References 6 publications

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Progress in Object Detection: An In-Depth Analysis of Methods and Use Cases

Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System

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