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Deep Learning Based Object Detection in Surveillance Video 4

Deep Learning Based Object Detection in Surveillance Video

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Arun, M.R. Anto Bennet, M. Satav, Sandip D. Manikandan, V. Gopi Anand, M. Dawande, Nitin Ashok Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Chennai India JSPM's Jscoe Pune Department of Information Technology Hadapsar India School of Engineering and Technology Jain University Department of Cse Bangalore India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India School of Engineering and Technology D y Patil University Department of Cse Pune India

ISBN: (纸本)9798350300888

Object Tracking from surveillance video has become a popular research area in the fields of image processing, video processing, and computer vision. the abilityto accurately identify specific objects from surveillance footage can have a significant impact on public safety and security. Our research employs YOLOv5, a state-of-the-art deep learning algorithm, for object detection in this domain. YOLOv5 is particularly effective in identifying objects in real time with high accuracy and speed. In this paper, we focus on using YOLOv5 for the detection of common objects in our daily lives. With various crimes and thefts occurring in society, it is challenging to guarantee everyone's safety on the streets, highways, or roadsides. therefore, our research aims to address this issue by exploring how we can reduce manual labor and improve safety through the detection of specific objects in different scenarios. For instance, we can identify individuals based on their attire or if they are carrying a weapon or dangerous item. By fine-tuning our model, we can accurately detect and identify the required objects, allowing us to take appropriate action and enhance public safety. Overall, our research shows the potential of YOLOv5 for object detection in specific scenarios and demonstrates how it can be used to enhance public safety and security. © 2023 IEEE.

关键词： Object detection

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An Anti-epidemic Delivery Trolley based on YOLOv5 image recognition of Room Numbers and QR Codes

An Anti-epidemic Delivery Trolley based on YOLOv5 Image Reco...

引用

computer vision, image and Deep Learning (CVIDL), international conference on

作者： Saibiao Jiang Jianan Fan Zhijin Sun Baoru Deng School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Computer Engineering Zhuhai College of Science and Technology Zhuhai China

Since the massive spread of the COVID-19 epidemic in 2020 and the recent spread of influenza, the contactless delivery trolley has met the demands of the government for logistics against epidemics and the needs of logistics companies for deliveries. this paper designs a recognition method based on YOLOv5 (You Only Look Once version 5) for detecting room numbers and QR codes for the needs of anti-epidemic delivery trolleys and implements tasks such as stopping the anti-epidemic trolley at a specified room number and opening different warehouses according to different QR code information. the experiments improved the recognition by filtering the pepper noise through mean filtering. Experimental results show an increase of 2.105 % in the recognition rate of pre-processed QR codes, with an average accuracy rate of 98.9%. the delivery trolley also achieves a stable recognition rate of around 98.81% for room numbers.

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CNN-Based Video Surveillance for Fire and Localization Detection 4

CNN-Based Video Surveillance for Fire and Localization Detec...

引用

4th international conference on Cognitive Computing and Information processing, CCIP 2022

作者： Sudharani Shirwaikar, Rudresh Deepak Mathews, Lincy Ramaiah Institute of Technology Department of Information Science Engineering Karnataka Bangalore560054 India Agnel Institute of Design and Technology Department of Computer Engineering Goa Bardez403507 India

ISBN: (纸本)9781665456487

Convolutional neural networks (CNNs) have shown success in picture classification and other computer vision issues. the use of CNN in the fire recognition technique will greatly enhance detection accuracy, resulting in fewer fire tragedies and societal and environmental consequences. However, as inference requires a lot of memory and computing power, a significant problem is implementing CNN-based fire sensing devices in an actual video network. We describe a new, energy-efficient, Fire detection, location, and semantic understanding using a computationally efficient CNN architecture scenario based on the Squeeze Net design. It applies compact convolutional kernels and avoids huge, completely linked layers to save computational load. Despite its minimal processing requirements, results of the experiment show that our suggested approach achieves accuracy = 99.7%, F1-score = 98.49%, Precision = 98.99%, and Recall = 98.00%. Furthermore, by considering the specific characteristics of the circumstance at hand as well as the variety of fire data, the study shows how the efficiency and accuracy of the fire detection model. © 2022 IEEE.

关键词： Fires

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Hand Sign recognition using Deep Convolutional Neural Network 4

Hand Sign Recognition using Deep Convolutional Neural Networ...

引用

4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Naveenkumar, M. Srithar, S. Kalyan, G Ramesh Vetrimani, E. Alagumuthukrishnan, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Sns College of Technology Department of Computer Science and Engineering Coimbatore India Presidency University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Computer Science and Engineering Telangana State Hyderabad India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Sign language is the most complex language to understand by the end-user without knowing the meaning of the sign and it depends on the special gesture motion. the gesture marks are characterized by hands with aid by facial appearance and body position. In this article, Gesture recognition is proposed for static sign language using Deep Learning with image processing. the involvement contains dual solutions to the problem. One is resized with Bicubic static American Sign Language binary images. Besides that, good recognition results in of detection the borderline hand using the various edge detection techniques. Another solution is to classify the alpha characters of sign language using a Convolution Neural Network (CNN). Owing to the anticipated method, test accuracy of 96% and F1-Score of 99% have achieved for 10 different hand gesture classifications. © 2022 IEEE.

关键词： Binary images

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Detection of Motion Vector-Based Stegomalware in Video Files 1

引用

international conference on computer vision and Machine Intelligence, CVMI 2022

作者： Nair, Sandra V. S. Arun Raj Kumar, P. Department of Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Cybercriminals are increasingly using steganography to launch attacks on devices. the cyberattack is more threatening as steganography hides the embedded malware, if any, making it harder to detect by various anti-virus tools. Such malware is called stegomalware. Since video files are larger and have a complex structure, they also have a high capacity for hiding malware. Motion vector (MV)-based steganography techniques do not cause much distortion in video files. therefore, it remains to be one of the leading video steganography techniques. this paper deals with a lightweight solution for MV-based stegomalware detection in video files. Our model is compatible with state-of-the-art video coding standards having variable macroblock sizes and different motion vector resolutions. the proposed method obtained an accuracy of 95.8% on testing H.264 videos with various embedding rates. the 81-D spatial and temporal features result in the high performance of the proposed model. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Video signal processing

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BnSepCNN: Convolutional Neural Network for P300 Detection

BnSepCNN: Convolutional Neural Network for P300 Detection

引用

pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Maohua Liu Fred R. Beyette School of Electrical and Computer Engineering College of Engineering University of Georgia Athens USA

Convolutional neural network (CNN) has shown impressive advantages in various applications like computer vision and speech recognition. However, CNN’s applications in P300 detection are still in primary stage. Different from the image and voice data, P300 data have very low SNR and high subject variability so that traditional CNN architectures do not perform well at P300 detection. In this paper, we proposed BnSepCNN based on batch normalization and separable convolution, aiming at reaching high rate for P300 detection with effective CNN architecture. We compared its performance with state-of-the-art architectures: SepConv1D and BN 3 . Our experimental results show that, BnSepCNN performs better than SepConv1D which is based on separable convolution, and it has far fewer parameters than BN 3 which is based on batch normalization.

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Probing Attention-Driven Normalizing Flow Network for Low-Light image Enhancement 27th

Probing Attention-Driven Normalizing Flow Network for Low-L...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Singh, Siddharth Mehta, Nancy Prakash, K.N. Vipparthi, Santosh Kumar Murala, Subrahmanyam CVPR Lab Indian Institute of Technology Ropar Rupnagar India Vision Lab CAIDAS & IFI University of Wuerzburg Würzburg Germany SR Gudlavalleru Engineering College Vijayawada India CVPR Lab School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9783031781247

Existing low-light image enhancement approaches based upon pixel-wise reconstruction losses are inadept at capturing the complex distribution of well-exposed images, resulting in residual noise, insufficient illuminance, and artifacts. Additionally, the mapping relationship between weakly-illuminated and normally exposed images is one-to-many, making low-light image enhancement a vastly ill-posed problem. In this work, we probe into this one-to-many relationship via an attention and frequency driven normalizing flow network by minimizing the negative log-likelihood loss. the proposed model comprises of two parts: a dual-attention-oriented frequency encoder network and an invertible network which inputs the conditional low-light images and changes the mapping of the complex distribution of well-light images to simpler Gaussian distribution. the proposed model not only utilizes the spatial information inherent in the image for improving the contrast but also extracts the frequency information for preserving the intricate details. To sum up, the distribution of the well-exposed images can be characterized better, and the overall enhancement mechanism becomes analogous to being restrained by a loss function which defines the manifold structure of natural images during the training. Detailed experiment analysis on a variety of challenging low-light images exemplifies the potency of the model and shows its primacy over the state-of-the-art in terms of enhanced quality and efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mapping

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Real-time Enhancement Algorithm of Low-light image Based on Zero-DCE 4

Real-time Enhancement Algorithm of Low-light Image Based on ...

引用

4th international conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Zhang, Qian Bai, Enrui Shao, Mingwen Liang, Hong Yang, Junlong China

ISBN: (纸本)9798350321951

At present, all walks of life, such as industry, transportation, medical care, etc., can use computer vision technology to promote the development of the industry and solve the problems of the industry. However, some industries have complex operation scenarios and are easily affected by weather and other factors, resulting in blurred and dimmed images. In response to such problems, many image enhancement algorithms have emerged and achieved good results. However, most of these methods perform image enhancement on all video images, which leads to deliberate enhancement of some normal images, resulting in overexposure;at the same time, it also causes waste of computing resources, and it is difficult to guarantee the real-time performance of the algorithm. In view of the above content, this paper first proposes a low-light image judgment method to judge whether the current video frame needs to be preprocessed. In addition, through experiments and comparison of the current mainstream low-light image preprocessing algorithms, the Zero-DeE algorithm that is more suitable for field use is selected, and the self-made drilling multi-exposure sequence data set is combined to train the optimization model, which has both subjective processing effect and objective index evaluation raised. © 2022 IEEE.

关键词： image enhancement

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Algorithm for Urban Air Mobility Object recognition Based on Spiking Neural Networks 4

Algorithm for Urban Air Mobility Object Recognition Based on...

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Qu, Jingyi Li, Haohao Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9781510672505

In recent years, a large number of intelligent unmanned aerial vehicles (UAVs) have emerged in people's vision, leading to the development of a new safe and efficient urban air mobility (UAM) system, which is suitable for both manned and cargo scenarios. Driven by the market, this service is expected to become a new trend in the development of civil aviation. the rapid development of deep learning has brought prospects to urban air traffic, but the increasing data volume and model complexity pose challenges in terms of resource consumption and model deployment. A method of applying spiking neural network (SNN) algorithms in UAM is proposed in this paper, utilizing spike-based transmission to significantly reduce energy consumption compared to deep learning algorithms. To address the issue of missing urban air traffic data sets, we build a complex urban air traffic data set comprising 10 categories such as pedestrians, vehicles, buildings, traffic signals, billboards, garbage bins, unmanned aerial vehicles, hot air balloons, cats, and dogs. In the network model, we introduce the biological neurons such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), achieving the highest accuracy of 81.937% and 82.772%, respectively. Based on the LIF neuron, we propose the Self-Learning Leaky Integrate-and-Fire (SLLIF) neuron, which autonomously learns stimulus-input ratio relationships to better align with the brain's automatic optimization mechanism, achieving a recognition accuracy of 85.484%. Furthermore, we re-evaluate the selection of the hyper parameter time steps and choose suitable values for different neural network models to reduce resource consumption while maximizing accuracy. © 2023 SPIE.

关键词： Air mobility

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Ocular Eye Disease Prediction Using Machine Learning 15th

Ocular Eye Disease Prediction Using Machine Learning

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kongala, Lavanya Bikku, thulasi Preethi, B. Zaveriya Ruman, S.K. Susmitha, P. Sowjanya, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Vengal522503 India Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9783031810794

the human eye, often considered the most crucial sense organ for perceiving and understanding the world, plays a fundamental role in our ability to see and interpret our surroundings. However, vision can be significantly impacted by various ocular eye disorders, which pose significant challenges to our visual capabilities. Among these disorders, one of the most prevalent conditions is cataract, a condition characterized by cloudiness in the lens of the eye, resulting in impaired vision and a blurred perception of objects. Typically, cataracts are more commonly found in individuals of advanced age due to natural aging processes. Identifying and diagnosing ocular eye illnesses can be a complex and challenging task, often requiring sophisticated techniques and tools to aid in the process. In recent research endeavors, automated learning algorithms have emerged as valuable tools for assisting in the detection of ocular eye disorders. these algorithms, such as Convolutional Neural Networks (CNNs), are employed alongside image pre-processing techniques to analyze and interpret ocular data. By leveraging the power of machine learning, researchers have been able to develop and refine predictive models for ocular diseases. To evaluate the accuracy of disease prediction models, various design pattern graphs can be utilized. these graphs serve as visual representations, showcasing the performance and reliability of the algorithms in predicting ocular eye disorders. By employing these advanced technological approaches, researchers and medical professionals can enhance their ability to identify and diagnose ocular conditions, ultimately leading to more effective treatments and interventions for individuals affected by these diseases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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