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LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

LightTrack: A Generic Framework for Online Top-Down Human Po...

引用

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

作者： Ning, Guanghan Pei, Jian Huang, Heng JD Finance Amer Corp Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9781728193601

In this paper, we propose a simple yet effective framework, named LightTrack, for online human pose tracking. Existing methods usually perform human detection, pose estimation and tracking in sequential stages, where pose tracking is regarded as an offline bipartite matching problem. Our proposed framework is designed to be generic, efficient and truly online for top-down approaches. For efficiency, Single-Person Pose Tracking (SPT) and Visual Object Tracking (VOT) are incorporated as a unified online functioning entity, easily implemented by a replaceable single-person pose estimator. To mitigate offline optimization costs, the framework also unifies SPT with online identity association and sheds first light upon bridging multiperson keypoint tracking with Multi-Target Object Tracking (MOT). Specifically, we propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a Re-ID module. In contrary to other Re-ID modules, we use a graphical representation of human joints for matching. The skeleton-based representation effectively captures human pose similarity and is computationally inexpensive. It is robust to sudden camera shifts that introduce human drifting. The proposed framework is general enough to fit other pose estimators and candidate matching mechanisms. Extensive experiments show that our method outperforms other online methods and is very competitive with offline state-of-the-art methods while maintaining higher frame rates. Code and models are publicly available at https://***/Guanghan/lighttrack.

关键词： computer vision

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CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

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

作者： Wang, Zhengwei She, Qi Chalasani, Tejo Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781728193601

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： computer vision

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The 1st Agriculture-vision Challenge: Methods and Results

The 1st Agriculture-Vision Challenge: Methods and Results

引用

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

作者： Chiu, Mang Tik Xu, Xingqian Wang, Kai Hobbs, Jennifer Hovakimyan, Naira Huang, Thomas S. Shi, Honghui Wei, Yunchao Huang, Zilong Schwing, Alexander Brunner, Robert Dozier, Ivan Dozier, Wyatt Ghandilyan, Karen Wilson, David Park, Hyunseong Kim, Junhee Kim, Sungho Liu, Qinghui Kampffmeyer, Michael C. Jenssen, Robert Salberg, Arnt B. Barbosa, Alexandre Trevisan, Rodrigo Zhao, Bingchen Yu, Shaozuo Yang, Siwei Wang, Yin Sheng, Hao Chen, Xiao Su, Jingyi Rajagopal, Ram Ng, Andrew Van Thong Huynh Kim, Soo-Hyung Na, In-Seop Baid, Ujjwal Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Tang, Jianyu UIUC Urbana IL 61801 USA Intelinair Indianapolis IN USA Univ Oregon Eugene OR 97403 USA Agcy Def Dev Seoul South Korea DGIST Daegu South Korea Norwegian Comp Ctr Oslo Norway UiT Arctic Univ Norway Oslo Norway Tongji Univ Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chegg Inc Santa Clara CA USA Chonnam Natl Univ Gwangju South Korea Chosun Univ Gwangju South Korea SGGS Inst Engn & Technol Nanded India Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

The first Agriculture-vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task;the link can be found here.

关键词： Semantics Image segmentation pattern recognition conferences Agriculture Computational modeling computer vision

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

引用

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

作者： Ren Yang Radu Timofte Meisong Zheng Qunliang Xing Minglang Qiao Mai Xu Lai Jiang Huaida Liu Ying Chen Youcheng Ben Xiao Zhou Chen Fu Pei Cheng Gang Yu Junyi Li Renlong Wu Zhilu Zhang Wei Shang Zhengyao Lv Yunjin Chen Mingcai Zhou Dongwei Ren Kai Zhang Wangmeng Zuo Pavel Ostyakov Vyal Dmitry Shakarim Soltanayev Chervontsev Sergey Zhussip Magauiya Xueyi Zou Youliang Yan Pablo Navarrete Michelini Yunhua Lu Diankai Zhang Shaoli Liu Si Gao Biao Wu Chengjian Zheng Xiaofeng Zhang Kaidi Lu Ning Wang Thuong Nguyen Canh Thong Bach Qing Wang Xiaopeng Sun Haoyu Ma Shijie Zhao Junlin Li Liangbin Xie Shuwei Shi Yujiu Yang Xintao Wang Jinjin Gu Chao Dong Xiaodi Shi Chunmei Nian Dong Jiang Jucai Lin Zhihuai Xie Mao Ye Dengyan Luo Liuhan Peng Shengjie Chen Xin Liu Qian Wang Boyang Liang Hang Dong Yuhao Huang Kai Chen Xingbei Guo Yujing Sun Huilei Wu Pengxu Wei Yulin Huang Junying Chen Ik Hyun Lee Sunder Ali Khowaja Jiseok Yoon ETH Z&#x00FC rich rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://***/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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AI City Challenge 2020-computer vision for Smart Transportation Applications

AI City Challenge 2020-Computer Vision for Smart Transportat...

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

作者： Chang, Ming-Ching Chiang, Chen-Kuo Tsai, Chun-Ming Chang, Yun-Kai Chiang, Hsuan-Lun Wang, Yu-An Chang, Shih-Ya Li, Yun-Lun Tsai, Ming-Shuin Tseng, Hung-Yu SUNY Albany Albany NY 12222 USA Natl Chung Cheng Univ Minxiong Taiwan Univ Taipei Taipei Taiwan

ISBN: (纸本)9781728193601

We present methods developed in our participation of the AI City 2020 Challenge (AIC20) and report evaluation results in this contest. With the blooming of AI computer vision techniques, vehicle detection, tracking, identification, and counting all have advanced significantly. However, whether these technologies are ready for real-world smart transportation usage is still a open question. The goal of this work is to apply and integrate state-of-the-art techniques for solving the challenge problems under a standardized setup and evaluation. We participated all 4 AIC20 challenge tracks (T1 to T4). In T1 challenge, we perform vehicle counting by associating deep features extracted from Mask-RCNN detections and tracklets, followed by vehicle movement zone matching. In T2 challenge, we perform vehicle type and color classification and then rank matching vehicles using a PGAM re-id network. In T3 challenge, we proposed a new Multi-Camera Tracking Network (MTCN) that takes single-camera vehicle tracking as input, and performs multi-camera tracklet fusion and linking, by jointly optimizing the matching of vehicle appearance and physical features. In T4 challenge, we adopt a leading method based on perspective detection and spatial-temporal matrix discriminating, and improve it with background modeling for traffic anomaly detection. We achieved top-6 and top-4 performance for T3 and T4 challenges respectively in the AIC20 general leaderboard.

关键词： computer vision

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PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

PI-Net: A Deep Learning Approach to Extract Topological Pers...

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

作者： Som, Anirudh Choi, Hongjun Ramamurthy, Karthikeyan Natesan Buman, Matthew P. Turaga, Pavan Arizona State Univ Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA IBM Res Albany NY USA Arizona State Univ Coll Hlth Solut Tempe AZ 85287 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://***/anirudhsom/PI-Net.

关键词： Machine learning computer architecture computer vision Task analysis Topology Feature extraction Data models

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A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech recognition

A Study of Dropout-Induced Modality Bias on Robustness to Mi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yusheng Dai Hang Chen Jun Du Ruoyu Wang Shihao Chen Haotian Wang Chin-Hui Lee University of Science and Technology of China Hefei China Georgia Institute of Technology Atlanta America

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advanced Audio- Visual Speech recognition (AVSR) sys-tems have been observed to be sensitive to missing video frames, performing even worse than single-modality mod-els. While applying the common dropout techniques to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this study, we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fun-damental cause. Next, we present the Modality Bias Hy-pothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings, we propose a novel Multimodal Distribution Approxi-mation with Knowledge Distillation (MDA-KD)framework to reduce over-reliance on the audio modality, maintaining performance and robustness simultaneously. Finally, to address an entirely missing modality, we adopt adapters to dynamically switch decision strategies. The effective-ness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://***/dalision/ModalBiasAV5R.

关键词： Visualization computer vision Systematics Codes Speech recognition Switches Robustness

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MethaneMapper: Spectral Absorption Aware Hyperspectral Transformer for Methane Detection

MethaneMapper: Spectral Absorption Aware Hyperspectral Trans...

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conference on computer vision and pattern recognition (CVPR)

作者： Satish Kumar Ivan Arevalo ASM Iftekhar B S Manjunath Department of Electrical and Computer Engineering University of California Santa Barbara

Methane (CH 4 ) is the chief contributor to global climate change. Recent Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) has been very useful in quantitative mapping of methane emissions. Existing methods for analyzing this data are sensitive to local terrain conditions, often require manual inspection from domain experts, prone to significant error and hence are not scalable. To address these challenges, we propose a novel end-to-end spectral absorption wavelength aware transformer network, MethaneMapper, to detect and quantify the emissions. MethaneMapper introduces two novel modules that help to locate the most relevant methane plume regions in the spectral domain and uses them to localize these accurately. Thorough evaluation shows that MethaneMapper achieves 0.63 mAP in detection and reduces the model size (by 5×) compared to the current state of the art. In addition, we also introduce a large-scale dataset of methane plume segmentation mask for over 1200 AVIRIS-NG flight lines from 2015–2022. It contains over 4000 methane plume sites. Our dataset will provide researchers the opportunity to develop and advance new methods for tackling this challenging green-house gas detection problem with significant broader social impact. Dataset and source code link 1 1 https://***/UCSB-Vrl/methaneMapper-Spectral-Absorption-aware-Hyperspectral-Transformer-for-Methane-Detection.

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Trident Dehazing Network

Trident Dehazing Network

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

作者： Liu, Jing Wu, Haiyan Xie, Yuan Qu, Yanyun Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9781728193601

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy-free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature;the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder;and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： computer vision

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

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

作者： Casian Miron Alexandru Pasarica Radu Timofte MCC Resources S.R.L. "Gheorghe Asachi" Technical University Iasi Romania

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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