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A Technique for Human Upper Body Parts Movement Tracking

IETE JOURNAL OF RESEARCH 2023年第11期69卷 7874-7883页

作者： Kumar, Krishan Mishra, Abhya Dahiya, Sanjay Kumar, Ajay NIT Uttarakhand Comp Sci & Engn Pauri Garhwal India CDL State Inst Engn & Technol Comp Sci & Engn Sirsa India Usha Martin Univ Comp Sci & Engn Ranchi Bihar India

In this digital world, technology mainly revolves around multimedia content, which is massive. The existing techniques are inadequate to capture the movement of the human upper body parts in real-time. Therefore, a system is urgently required to track the human upper body parts movements from image and video content. The Viola-Jones algorithm based on a novel technique is proposed to detect upper body parts like the eyes and face in this work. Moreover, a face tracking algorithm is employed to track the movement of the upper body parts in real-time. The proposed model improves 13%, 18%, and 10% accuracy for face, eye, and body on Grimace dataset, Face94 Dataset, and Milborrow University of Cape Town (MUCT) dataset, respectively. Furthermore, it is also tested directly on the MATLAB software on a new image or Cam video. It takes less than 25 milliseconds per frame;hence, applicable for real-time applications.

关键词： Computer vision eye detection face detection image processing upper-body parts detection Viola-Jones algorithm

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real-time stereo semi-global matching for video processing using previous incremental information

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JOURNAL OF real-time image processing 2022年第1期19卷 205-216页

作者： Toledo, Jonay Lauer, Martin Stiller, Christoph Univ La Laguna San Cristobal la Laguna Spain Karlsruhe Inst Technol KIT Karlsruhe Germany

This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios.

关键词： Stereo vision Semi-global matching video processing 3D reconstruction real time

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real time FPGA Implementation of a High Speed for video Encryption and Decryption System with High Level Synthesis Tools

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 724-731页

作者： Alhomoud, Ahmed Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Rafha 91911 Saudi Arabia

The development of communication networks has made information security more important than ever for both transmission and storage. Since the majority of networks involve images, image security is becoming a difficult challenge. In order to provide real-time image encryption and decryption, this study suggests an FPGA implementation of a video cryptosystem that has been well -optimized based on high level synthesis. The MATLAB HDL coder and Vivado Tools from Xilinx are used in the design, implementation, and validation of the algorithm on the Xilinx Zynq FPGA platform. Low resource consumption and pipeline processing are well -suited to the hardware architecture. For real-time applications involving secret picture encryption and decryption, the suggested hardware approach is widely utilized. This study suggests an implementation of the encryption -decryption system that is both very efficient and areaoptimized. A unique high-level synthesis (HLS) design technique based on application -specific bit widths for intermediate data nodes was used to realize the proposed implementation. For HLS, MATLAB HDL coder was used to generate register transfer level RTL design. Using Vivado software, the RTL design was implemented on the Xilinx ZedBoard, and its functioning was tested in real time using an input video stream. The results produced are faster and more are a efficient (target FPGA has fewer gates than before) than those of earlier solutions for the same target board.

关键词： Security encryption decryption AES HDL coder high levelsynthesis FPGA Zynq7000

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Compressive sensing reconstruction of hyperspectral images based on codec space-spectrum joint dense residual network

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IET image processing 2023年第3期17卷 916-931页

作者： Xiao, Shuming Zhang, Ye Chang, Xuling Xu, Jiajia Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys 3888 Dong Nanhu Rd Changchun 130033 Peoples R China Univ Chinese Acad Sci Daheng Coll Beijing 100000 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun Peoples R China

The spatial and spectral information contained in the hyperspectral image (HSI) make it widely used in many fields. However, the sharp increase of HSI data brings enormous pressure to the data storage and real-time transmission. The research shows that hyperspectral compressive sensing (HCS) breaks through the bottleneck of the Nyquist sampling theorem, which can relieve the massive pressure on data storage and real-time transmission. Existing HCS methods try to design advanced compression sampling matrix or reconstruction algorithms, but cannot connect the two through a unified framework. To further improve the image reconstruction quality, a novel codec space-spectrum joint dense residual network (CDS2-DResN) is proposed. The CDS2-DResN is divided into block compression sampling part and reconstruction part. For block compression sampling, coded convolutional layer (CCL) is leveraged to compress and sample HSI. For measurements reconstruction, deconvolution layer is first leveraged to initially reconstruct HSI, and then build a space-spectrum joint network to refine the initial reconstructed HSI. Moreover, the CCL and reconstruction network are optimized via a unified framework, which can simplify the pre-processing and post-processing process of HCS. Extensive experiments have shown that CDS2-DResN has an excellent HCS reconstruction effect at measurement rates 0.25, 0.10, 0.04 and 0.01, respectively.

关键词： feature extraction block compression sampling part Nyquist sampling theorem hyperspectral image compression sampling matrix massive pressure Geophysics computing CDS2-DResN enormous pressure initial reconstructed HSI reconstruction network image and video coding Neural nets geophysical image processing coded convolutional layer Optical, image and video signal processing Computer vision and image processing techniques convolutional neural nets measurements reconstruction compressed sensing hyperspectral imaging compressive sensing reconstruction Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research reconstruction part HSI data excellent HCS reconstruction effect hyperspectral compressive sensing sample HSI image reconstruction space-spectrum joint network spatial information Geophysical techniques and equipment real-time transmission spectral information novel codec space-spectrum joint dense residual network image reconstruction quality data storage HCS methods

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time-attentive fusion network: An efficient model for online detection of action start

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IET image processing 2024年第7期18卷 1892-1902页

作者： Hu, Xuejiao Wang, Shijie Li, Ming Li, Yang Du, Sidan Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

Online detection of action start is a significant and challenging task that requires prompt identification of action start positions and corresponding categories within streaming videos. This task presents challenges due to data imbalance, similarity in boundary content, and real-time detection requirements. Here, a novel time-Attentive Fusion Network is introduced to address the requirements of improved action detection accuracy and operational efficiency. The time-attentive fusion module is proposed, which consists of long-term memory attention and the fusion feature learning mechanism, to improve spatial-temporal feature learning. The temporal memory attention mechanism captures more effective temporal dependencies by employing weighted linear attention. The fusion feature learning mechanism facilitates the incorporation of current moment action information with historical data, thus enhancing the representation. The proposed method exhibits linear complexity and parallelism, enabling rapid training and inference speed. This method is evaluated on two challenging datasets: THUMOS'14 and ActivityNet v1.3. The experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in terms of both detection accuracy and inference speed. Here, a novel time-Attentive Fusion Network (TAF-Net) is introduced to address the requirements of improved action detection accuracy and operational efficiency in the task of online detection of action start. The proposed model not only learns valuable sequence information for precise detection but its linear computational complexity and parallelism also contribute to a faster inference speed. image

关键词： computer vision feature extraction image processing video signal processing

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ENCODING-AWARE DEEP video SUPER-RESOLUTION FRAMEWORK 30

ENCODING-AWARE DEEP VIDEO SUPER-RESOLUTION FRAMEWORK

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30th IEEE International conference on image processing (ICIP)

作者： Kim, Sijung Lee, Ungwon Jeon, Minyong BLUEDOT Inc Seoul South Korea

ISBN: (纸本)9781728198354

video super-resolution(VSR) upscales a low-resolution video to the higher one. Most applications require compression of the super-resolved video due to limited internet bandwidth and storage capacity. However, most studies on VSR techniques have focused only on improving image quality, ignoring the impact of the compression process on visual quality. Consequently, even a VSR with good visual quality has a risk of significant loss of quality when serviced online or stored as a file. To address this problem, we propose an encoding-aware VSR framework. In the framework, we created a differentiable virtual codec to estimate the bit rate and used it for the loss function, which optimizes the super-resolved videos by considering the rate-distortion trade-off relationship and eventually leads to the prevention of visual quality degradation. According to the results, our real-time VSR model for x4 upscaling, trained with 1,191K parameters, yields a maximum gain of 13.2% over state-of-the-art VSR models based on the Bjontegaard delta rate.

关键词： video compression video super-resolution Motion vector information

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Efficient FPGA architecture to implement non-separable fast Fourier transform for image and video applications

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INTERNATIONAL JOURNAL OF ELECTRONICS 2023年第4期110卷 631-647页

作者： Sarkar, Sayantam Bhairannawar, Satish S. MVJ Coll Engn Dept Elect & Commun Engn Bangalore Karnataka India Shri Dharmasthala Manjunatheshwara Coll Engn & Te Dept Elect & Commun Engn Dharwad Karnataka India

Fast Fourier Transform (FFT) is widely used in image and video processing applications to convert the respective image or video frames into transform domain that is very helpful to extract the accurate features of that image or video frame for various real-time applications. In this paper, efficient non-separable 8-point FFT architecture (DIT-FFT) is proposed that is implemented on Spartan-6 (xc6slx45-3csg324) FPGA. The proposed architecture consists of Data Format Conversion, Addition, Subtraction, Multiplier Equivalent and D-FF blocks, respectively. The non-separable equations of 8-point DIT-FFT are derived from the respective Butterfly Diagram that is then implemented using basic logic gates, which optimises the hardware utilisations with the help of Complex Conjugate property. The constant multiplications present in the non-separable DIT-FFT equations are implemented through Adders and Shifters presents in Multiplier Equivalent block which further optimises the overall hardware utilisations. Moreover, the Q-format are used to increase the data accuracy of the architecture. The comparison results show that the proposed architecture is better than existing in different prospectives.

关键词： Fast Fourier transform (FFT) Field Programmable Gate Arrays (FPGA) hardware architecture Hardware Description Language (HDL) image and video processing

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ENHANCING INTUBATION ACCURACY: ADVANCED TRACHEAL SEGMENTATION TECHNIQUES IN video ENDOSCOPY 31

ENHANCING INTUBATION ACCURACY: ADVANCED TRACHEAL SEGMENTATIO...

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2024 International conference on image processing

作者： Oulefki, Adel Amira, Abbes Kurugollu, Fatih Trongtirakul, Thaweesak Agaian, Sos Mohammed, Menen Kassim Alshoweky, Mohammad Univ Sharjah UoS RISE Sharjah U Arab Emirates Univ Sharjah UoS Comp Sci Dept Sharjah U Arab Emirates Rajamangala Univ Technol Phra Nakhon RMUTP Bangkok Thailand CUNY Coll Staten Isl Comp Sci Dept Staten Isl NY USA

ISBN: (纸本)9798350349405;9798350349399

Tracheal intubation is a critical medical procedure involving the insertion of a tube into the trachea to maintain an open airway. While essential, this procedure carries significant risks, such as incorrect tube placement. Advances in visually guided intubation methods, like video laryngoscopy, have enhanced safety by enabling precise tracheal segmentation from endoscopic images. Our study introduces an innovative image enhancement technique for video endoscopy that significantly improves tracheal visibility and segmentation accuracy. This novel approach not only facilitates safer and more accurate intubation but also minimizes patient discomfort and procedural risks. Tested against the UoS Dataset and real patient data from thyroidectomy procedures, our method demonstrated superior performance, achieving a segmentation accuracy of 97%, a precision of 94%, and a recall of 99%. Our tailored method is computationally efficient, making it suitable for implementation on edge devices like Arduino, thereby enhancing intubation safety and efficiency in various medical settings.

关键词： video Endoscopy Medical Imaging Enhancement Trachea Segmentation Intubation

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An Edge-Side real-time video Analytics System With Dual Computing Resource Control

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IEEE TRANSACTIONS ON COMPUTERS 2023年第12期72卷 3399-3415页

作者： Hu, Chuang Lu, Rui Sang, Qianlong Liang, Huanghuang Wang, Dan Cheng, Dazhao Zhang, Jin Li, Qing Peng, Junkun Wuhan Univ Sch Comp Sci Wuhan 430000 Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Southern Univ Sci & Technol Shenzhen 518000 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Tsinghua Univ Beijing 100190 Peoples R China

video analytics systems conduct video preprocessing to filter out unnecessary frames and model inference using appropriately selected neural networks for high analytics speed. video preprocessing is instruction-intensive computing (IIC) executed by CPU, and model inference is data-intensive computing (DIC) executed by GPU. In this paper, we show the analytics accuracy of existing systems can largely vary in fields, caused by the dynamic IIC and DIC workloads of different contents in applications. Unfortunately, cameras have fixed CPU/GPU resources and cannot effectively adapt to workload dynamics. We develop Gemini, a new edge-side real-time video analytics system enhanced by a dual-image FPGA. We take the advantage of negligible image switching time of dual-image FPGAs, pre-configure one CPU image and one GPU image and elastically multiplex the dual CPU-GPU resources in time dimension. Gemini requires both hardware and software revisions. In hardware, we overcome challenges of hardware-dependent application development, low communication efficiency between the microprocessor and FPGA, and high programming complexity by hardware abstraction, asynchronous data transfer mechanism and stub-skeleton middleware. In software, we overcome the challenge of adapting to the dynamic workloads by a bandit learning approach. We implement Gemini and show that Gemini can improve the analytics accuracy to 90.35%.

关键词： Field programmable gate arrays Visual analytics real-time systems Analytical models Streaming media Graphics processing units Computational modeling Bandit learning dual-image FPGA video analytics accelerators middleware

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TWO-LEVEL INTRA PREDICTION USING HIGH-ORDER MACROPIXEL NEIGHBORS FOR PLENOPTIC video CODING 31

TWO-LEVEL INTRA PREDICTION USING HIGH-ORDER MACROPIXEL NEIGH...

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2024 International conference on image processing

作者： Vinh Van Duong Thuc Nguyen Huu Yim, Jonghoon Jeon, Byeungwoo Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea Samsung Elect Samsung Res Seoul South Korea

ISBN: (纸本)9798350349405;9798350349399

This paper introduces a novel intra-prediction scheme for coding plenoptic video which can effectively exploit large correlation between current and neighboring macropixel images. While the intra block copy method is well recognized as a promising coding tool for plenoptic video, it has fundamental issues like much searching time for block vectors (BVs) and more bits to encode these BVs into a bitstream. Our method can effectively solve them by pre-defining the prediction candidates to save encoding time and signaling only the index of prediction location instead of BVs to reduce the overhead bits for encoding BVs. Compared to HEVC, our method is experimentally shown to achieve an average bitrate gain of about 19.70% and 11.99% respectively under the AI-Main and RA-Main conditions. Moreover, better trade- off can be made between complexity and coding performance than existing methods.

关键词： Light field plenoptic video video coding intra prediction

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