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Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System

Robust Automatic Motorcycle Helmet Violation Detection for a...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Duong Nguyen-Ngoc Tran Long Hoang Pham Hyung-Joon Jeon Huy-Hung Nguyen Hyung-Min Jeon Tai Huu-Phuong Tran Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Video surveillance-based automatic detection of motorcycle helmet usage can enhance the effectiveness of educational and enforcement initiatives aimed at boosting road safety. Current detection methods, however, have room for enhancement, such as the inability to pinpoint individual motorcycles or differentiate between drivers and passengers in terms of helmet usage. This paper introduces a framework designed to detect and identify individual motorcycles while recording specific helmet usage for riders. The proposed classification approach for helmet usage demonstrates increased efficiency in comparison to previous research. Our findings highlight the exceptional accuracy of deep learning, with our method achieving a score of 0.7754 on the AI City 2023 Challenge Track 5 public leaderboard.

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From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

From Evaluation to Verification: Towards Task-oriented Relev...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lyssenko, Maria Gladisch, Christoph Heinzemann, Christian Woehrle, Matthias Triebel, Rudolph Robert Bosch GmbH Corp Res Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany German Aerosp Ctr DLR Munchener Str 20 D-82234 Wessling Germany

ISBN: (纸本)9781665448994

Whenever a visual perception system is employed in safety-critical applications such as automated driving, a thorough, task-oriented experimental evaluation is necessary to guarantee safe system behavior. While most standard evaluation methods in computer vision provide a good comparability on benchmarks, they tend to fall short on assessing the system performance that is actually relevant for the given task. In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they are insensitive to important physical cues including distance, speed, and direction of motion. Therefore, we investigate so-called relevance metrics, where specific domain knowledge is exploited to obtain a task-oriented performance measure focusing on distance in this initial work. Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge. Our first results indicate a linear decrease of the IoU related to the pedestrians' distance, leading to the proposal of a first relevance metric that is also conditioned on the distance.

关键词： Measurement Meters computer vision System performance pattern recognition Proposals Motion measurement

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Initialization and Transfer Learning of Stochastic Binary Networks from Real-Valued Ones

Initialization and Transfer Learning of Stochastic Binary Ne...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Livochka, Anastasiia Shekhovtsov, Alexander Ukrainian Catholic Univ Lvov Ukraine Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781665448994

We consider the training of binary neural networks (BNNs) using the stochastic relaxation approach, which leads to stochastic binary networks (SBNs). We identify that a severe obstacle to training deep SBNs without skip connections is already the initialization phase. While smaller models can be trained from a random (possibly data-driven) initialization, for deeper models and large datasets, it becomes increasingly difficult to obtain non-vanishing and low variance gradients when initializing randomly. In this work, we initialize SBNs from real-valued networks with ReLU activations. Real valued networks are well established, easier to train and benefit from many techniques to improve their generalization properties. We propose that closely approximating their internal features can provide a good initialization for SBN. We transfer features incrementally, layer-by-layer, accounting for noises in the SBN, exploiting equivalent reparametrizations of ReLU networks and using a novel transfer loss formulation. We demonstrate experimentally that with the proposed initialization, binary networks can be trained faster and achieve a higher accuracy than when initialized randomly.

关键词： Training computer vision conferences Transfer learning Neural networks Stochastic processes pattern recognition

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Learned Video Compression with Intra-Guided Enhancement and Implicit Motion Information

Learned Video Compression with Intra-Guided Enhancement and ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zou, Nannan Zhang, Honglei Cricri, Francesco Tavakoli, Hamed R. Lainema, Jani Aksu, Emre Hannuksela, Miska Rahtu, Esa Nokia Technol Tampere Finland Tampere Univ Tampere Finland

ISBN: (纸本)9781665448994

Although learned approaches to video compression have been proposed with promising results, hand-engineered video codecs are still unbeaten. On the other hand, learned image compression has already surpassed traditional image codecs. In this paper, we propose a learned video compression system that mimics part of the pipeline of traditional codecs while leveraging learned image compression. It comprises two main modules: a learned intra-frame compression module, and a learned inter-frame compression module that is conditioned on intra-coded frames. These modules use separate learned probability models for entropy coding. The intra-frame codec uses a variant of nonlocal attention layers. Regarding the inter-frame codec, we propose an implicit motion information mechanism, and an enhancement of the inter-frame predictions by leveraging the high quality information of intra-coded frames. On the learned probability model side, we propose to use the reference frames as additional conditioning information. We used this system as our submitted entry for the 2021 Challenge on Learned Image Compression (CLIC). In our experiments, we show the effectiveness of our system and its components via a set of ablation studies.

关键词： computer vision Image coding conferences Computational modeling Pipelines MIMICs Video compression

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SuperLoRA: Parameter-Efficient Unified Adaptation for Large vision Models

SuperLoRA: Parameter-Efficient Unified Adaptation for Large ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiangyu Chen Jing Liu Ye Wang Pu Perry Wang Matthew Brand Guanghui Wang Toshiaki Koike-Akino University of Kansas Lawrence KS USA Mitsubishi Electric Research Laboratories (MERL) Cambridge MA USA Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models for computer vision. This paper proposes a generalized framework called SuperLoRA that unifies and extends different LoRA variants, which can be realized under different hyper-parameter settings. Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance, with up to 10-fold gain in parameter efficiency for transfer learning tasks.

关键词： computer vision Adaptation models Tensors Computational modeling Large language models conferences Transfer learning

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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Verma, Sagar Panigrahi, Akash Gupta, Siddharth Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer Paris France Granular AI Somerville NJ USA

ISBN: (纸本)9781665448994

Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period. This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://***/qfabric

关键词： Deep learning Urban areas Predictive models Benchmark testing Metadata Fabrics pattern recognition

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction pattern recognition

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Self-supervised multi-image super-resolution for push-frame satellite images

Self-supervised multi-image super-resolution for push-frame ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ngoc Long Nguyen Anger, Jeremy Davy, Axel Arias, Pablo Facciolo, Gabriele Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Kayrros SAS Paris France

ISBN: (纸本)9781665448994

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR) from bursts of push-frame images as a way to increase the resolution and reduce the noise of their products while maintaining a lower cost of operation. Most MISR techniques are currently based on the aggregation of samples from registered low resolution images. A promising research trend aimed at incorporating natural image priors in MISR consists in using data-driven neural networks. However, due to the unavailability of ground truth high resolution data, these networks cannot be trained on real satellite images. In this paper, we present a framework for training MISR algorithms from bursts of satellite images without requiring high resolution ground truth. This is achieved by adapting the recently proposed frame-to-frame framework to process bursts of satellite images. In addition we propose an architecture based on feature aggregation that allows to fuse a variable number of frames and is capable of handling degenerate samplings while also reducing noise. On synthetic datasets, the proposed self-supervision strategy attains results on par with those obtained with a supervised training. We applied our framework to real SkySat satellite image bursts leading to results that are more resolved and less noisy than the L1B product from Planet.

关键词： Training Satellites Planets Superresolution Neural networks computer architecture Optical imaging

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On the Application of Binary Neural Networks in Oblivious Inference

On the Application of Binary Neural Networks in Oblivious In...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Samragh, Mohammad Hussain, Siam Zhang, Xinqiao Huang, Ke Koushanfar, Farinaz Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781665448994

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime -accuracy tradeoff of BNNs in a single -shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to CryptFlow2, the state-of-the-art in oblivious inference of non binary DNNs, our approach reaches 2x faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2x to llx faster inference while obtaining higher accuracy.

关键词： Training computer vision Data analysis Computational modeling Face recognition Neural networks Data models

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Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations

Learning Melanocytic Proliferation Segmentation in Histopath...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Kechun Mokhtari, Mojgan Li, Beibin Nofallah, Shima May, Caitlin Chang, Oliver Knezevich, Stevan Elmore, Joann Shapiro, Linda Univ Washington Seattle WA 98195 USA Isfahan Univ Med Sci Esfahan Iran Dermatopathol Northwest Bellevue WA USA VA Puget Sound Seattle WA USA Pathol Associates Inc Clovis CA USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781665448994

Melanoma is the third most common type of skin cancer and is responsible for the most skin cancer deaths. A diagnosis of melanoma is made by the visual interpretation of tissue sections by a pathologist, a challenging task given the complexity and breadth of melanocytic lesions and the subjective nature of biopsy interpretation. We leverage advances in computer vision to aid melanoma diagnosis by segmenting potential regions of lesions on digital images of whole slide skin biopsies. In this study, we demonstrate a Mask-R-CNN-based segmentation framework for such a purpose. To alleviate the cost of data annotation, we leverage a sparse annotation pipeline. Our model can be trained on sparse and noisy labels and achieves state-of-the-art performance in identifying melanocytic proliferations, producing a segmentation with Dice score 0.719, mIOU 0.740 and overall pixel accuracy 0.927.

关键词： Image segmentation computer vision Visualization Annotations Biopsy Pipelines Melanoma

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