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Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification

Seq-Masks: Bridging the gap between appearance and gait mode...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Chang, Zhigang Yang, Zhao Chen, Yongbiao Zhou, Qin Zheng, Shibao Shanghai Jiao Tong Univ Inst Image Proc & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781728185514

Video-based person re-identification (Re-ID) aims to match person images in video sequences captured by disjoint surveillance cameras. Traditional video-based person Re-ID methods focus on exploring appearance information, thus, vulnerable against illumination changes, scene noises, camera parameters, and especially clothes/carrying variations. Gait recognition provides an implicit biometric solution to alleviate the above headache. Nonetheless, it experiences severe performance degeneration as camera view varies. In an attempt to address these problems, in this paper, we propose a framework that utilizes the sequence masks (SeqMasks) in the video to integrate appearance information and gait modeling in a close fashion. Specifically, to sufficiently validate the effectiveness of our method, we build a novel dataset named MaskMARS based on MARS. Comprehensive experiments on our proposed large wild video Re-ID dataset MaskMARS evidenced our extraordinary performance and generalization capability. Validations on the gait recognition metric CASIA-B dataset further demonstrated the capability of our hybrid model. Our codes and dataset MaskMARS will be open-sourced as a strong baseline.

关键词： Multi-modal Fusion Video-based Person Re-ID Gait Recognition appearance model

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Quantizing Neural Networks with Knowledge Distillation for Efficient Video Quality Assessment

Quantizing Neural Networks with Knowledge Distillation for E...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiayuan Yu Yingming Li College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

No-Reference Video Quality Assessment (NR-VQA) refers to evaluating the perceptual quality of videos without access to the original reference videos. However, existing deep NR-VQA methods often entail significant computational overhead, posing challenges in deploying these models in practical applications. In this paper, we focus on developing lightweight no-reference video quality assessment models and propose an Enhanced Quantization with Knowledge Distillation (EQKD) strategy to perform model compression while maintaining competitive performance. Specifically, we first introduce quantization-aware training in the VQA model parameter learning, which significantly reduces the memory footprint of the VQA models. Additionally, we incorporate knowledge distillation into the training process to further enhance the performance. The experimental results on three public datasets demonstrate the superiority of the proposed models over the state-of-the-art.

关键词： Training Knowledge engineering Quantization (signal) visual communication Computational modeling image processing Neural networks Quality assessment Video recording

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Comparitive Analysis of Various Transfer Learning Apporaches in Deep CNNs for image Classification 6th

Comparitive Analysis of Various Transfer Learning Apporaches...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Tyagi, Arnav Khandelwal, Rishabh Shelke, Nitin Arvind Singh, Jagendra Rajpal, Dev Gaware, Ishaan Rajendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9783031530814

Deep learning has become an effective approach over the past few years to addressing intricate computer vision problems, and Convolutional Neural Networks (CNNs) have been the primary driving force behind this progress. Developing CNNs, however, comes with the obstacle of requiring huge, labeled datasets. Gathering and annotating a large dataset for any specific job is costly and time-consuming. To overcome this challenge, researchers can employ transfer learning, a technique that involves using pre-trained deep learning models on extensive datasets. This study primarily aims to investigate the application of various transfer learning methods in conjunction with Deep Convolutional Neural Networks (CNNs) for image classification. The research utilizes the visual Object Classes Challenge 2012 (VOC2012) dataset as the foundation for its analysis. To classify a diverse range of object images, the study applies well-established transfer learning techniques, specifically fine-tuning pre-trained CNN models. Model performance is assessed through metrics like FPS (frames per second) and mAP% (mean average precision). A variety of models, including VGG19, SqueezeNet, YOLOv7, and Inception-ResNet-v2, are tested to determine the most suitable model for the dataset. The study’s findings demonstrate that employing transfer learning with CNNs can substantially enhance image classification accuracy on the dataset while reducing model development time. Notably, the Inception-ResNet-v2 model emerged as the top-performer, achieving an mAP score of 77%. This research underscores the potential of transfer learning as a powerful tool in the realm of image classification. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural image Compression

Semantic Ensemble Loss and Latent Refinement for High-Fideli...

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IEEE visual communications and image processing (VCIP)

作者： Daxin Li Yuanchao Bai Kai Wang Junjun Jiang Xianming Liu Faculty of Computing Harbin Institute of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually displeasing artifacts, such as blurring, color shifting, and texture loss, thereby compromising perceptual quality of images. To address these issues, this study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss, to enhance the perceptual quality of image reconstructions. Additionally, we have implemented a latent refinement process to generate content-aware latent codes. These codes adhere to bit-rate constraints, and prioritize bit allocation to regions of greater importance. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.

关键词： visualization image coding Codecs Codes Quantization (signal) visual communication image color analysis Semantics Bit rate image reconstruction

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image Enhancement Techniques Applied to Magnetic Resonance images: Multiple sclerosis 6

Image Enhancement Techniques Applied to Magnetic Resonance I...

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6th International conference on Advanced Technologies for Signal and image processing, ATSIP 2022

作者： Mnassri, Besma Echtioui, Amira Kallel, Fathi Dammak, Mariem Mhiri, Chokri Hamida, Ahmed Ben Atms Lab Advanced Technologies for Medicine and Signals Enis Sfax University Sfax Tunisia National School of Electronics and Communications Sfax University Sfax Tunisia Neurology Department Chu Habib Bourguiba Sfax Tunisia

ISBN: (纸本)9781665451161

As the most crucial step in image preprocessing techniques, Contrast enhancement (CE) is utilized to improve low contrast images quality with the aim to ameliorate the image's visual appearance for later automated processing (detection, segmentation and recognition). Due to this importance, several techniques were proposed to treat this topic. The present manuscript proposes comparative research across some contrast enhancement methods mainly: Global Histogram Equalization (GHE), Adaptive Gamma Correction (AGC) and knee function and gamma correction based on Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) method (KGC-DWT-SVD). These methods had been applied on brain and spinal cord MR (Magnetic Resonance) images of patients affected with multiple sclerosis. The obtained results had been evaluated according to the following measurement metrics: the entropy (H), peak signal to noise ratio (PSNR), mean squared error (MSE), structure similarity index measurement (SSIM) and feature similarity index measurement (FSIM). © 2022 IEEE.

关键词： Singular value decomposition

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2022 IEEE 14th International conference on Wireless communications and Signal processing, WCSP 2022

2022 IEEE 14th International Conference on Wireless Communic...

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14th IEEE International conference on Wireless communications and Signal processing, WCSP 2022

ISBN: (纸本)9781665450850

The proceedings contain 216 papers. The topics discussed include: block propagation in blockchain-based 6g network: from a communication perspective;location-based reliable sharding in blockchain-enabled fog computing networks;application-based cooperative content caching via tensor domain adaptation networks;power coverage optimization in cellular radio networks using neural architecture search;blockchain-enabled edge computing framework for hierarchic cluster-based federated learning;joint mining offloading and computing resource optimization in mobile blockchain system;low-cost traffic perception for road detector data estimation: a deep learning approach;a lightweight network with lane feature enhancement for multilane drivable area detection;large kernel attention hashing for efficient image retrieval;machine learning-based multi-classification for first-episode schizophrenics, ultra-high risk schizophrenics, and healthy controls;underwater image restoration based on an improved underwater image formation model;efficient shared feature learning for cross-modality person re-identification;and building scale-attentive visual transformers for intelligent electric power system inspection.

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Anchoring Vision and Language Knowledge for Weakly Supervised Group Activity Recognition

Anchoring Vision and Language Knowledge for Weakly Supervise...

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IEEE visual communications and image processing (VCIP)

作者： Muhammad Adi Nugroho Jinyoung Park Donguk Kim Changick Kim Korea Advanced Institute of Science and Technology (KAIST)

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The emergence of Foundation Vision-Language Models (VLMs) has ignited a surge of research in the computer vision field due to their robust baseline performance. Inspired by this, we propose the Anchoring Vision-Language Network (AnViL-Net), which integrates a vision language model for the challenging task of Weakly-Supervised Group Activity Recognition (WSGAR). Our network effectively incorporates VLMs into WSGAR, addressing the challenges posed by dynamic actor motions and domain-specific activity classes. AnViL-Net leverages highly generalized VLM vision features as anchors for extracting visual features. Additionally, semantically meaningful VLM language features serve as anchors for inferring the semantic relationships between actors and their activities. We demonstrate the effectiveness of AnViL-Net on multiple group activity datasets, achieving competitive state-of-the-art results.

关键词： visualization Computer vision image recognition visual communication Computational modeling Semantics Dynamics Activity recognition Feature extraction Surges

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Lookup Register-Tables with Interpolation for Effective image Transformation on x86/64 CPUs

Lookup Register-Tables with Interpolation for Effective Imag...

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IEEE visual communications and image processing (VCIP)

作者： Hirokazu Kamei Soichiro Honda Kohei Hayashi Yoshihiro Maeda Norishige Fukushima Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan College of Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Lookup tables (LUTs) are commonly used to speed up image processing by handling complex mathematical functions like sine and exponential calculations. They are used in various applications such as camera image processing, high-dynamic range imaging, and edge-preserving filtering. However, due to the increasing gap between computing and input/output performance, LUTs are becoming less effective. Even though specific circuits like SIMD can improve LUT efficiency, they still need to bridge the performance gap fully. The gap makes it difficult to choose between direct numerical and LUT calculations. For this problem, a register-LUTs method with the nearest neighbor was proposed; however, it is limited for functions with narrow-range values approaching zero. In this paper, we propose a method for using register LUTs to process images efficiently over a wide range of values. Our contributions include proposing register-LUT with linear interpolation for efficient computation, using a smaller data type for further efficiency, and suggesting an efficient data retrieving method.

关键词： Interpolation image transformation Accuracy visual communication Single instruction multiple data Filtering image edge detection Stacking Table lookup Registers

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Multi-View image Rectification for UAV-Captured image Sequences

Multi-View Image Rectification for UAV-Captured Image Sequen...

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IEEE visual communications and image processing (VCIP)

作者： Shun Zou Xin Jin Yihui Fan Shenzhen International Graduate School Tsinghua University Shenzhen China

The image sequences captured by Unmanned Aerial Vehicles (UAVs) can be applied to many computer vision tasks. However, due to the instability of UAV flight, the captured image sequences will deviate from the preset trajectory and pose, which reduce the quality of subsequent applications such as panoramic image stitching. In this paper, a novel method is proposed to rectify UAV-captured image sequences by transforming the images to a regular trajectory with the uniform pose. First, to minimize the total transformation deviation, virtual regular camera trajectory is derived by minimizing the global error of coordinates between actual and virtual camera trajectories. Then, camera-pose-relevant local homography is proposed by inserting the camera pose into local homography to transform the images to the derived virtual trajectory with the uniform pose and correct translation parallax. The experimental results demonstrate the effectiveness of the proposed rectification algorithm from both theoretical and application levels.

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