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arXiv 2019年

作者： Dendorfer, Patrick Rezatofighi, Hamid Milan, Anton Shi, Javen Cremers, Daniel Reid, Ian Roth, Stefan Schindler, Konrad Leal-Taixa, Laura Dynamic Vision and Learning Group at Tum Munich Germany Australian Institute for Machine Learning and School of Computer Science at University of Adelaide. Amazon Berlin Germany Photogrammetry and Remote Sensing Group at Eth Zurich Switzerland Computer Vision Group at Tum Munich Germany Department of Computer Science Technische Universität Darmstadt Germany

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15 [5], MOT16 [8], and MOT17 [8] have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the computer vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： computer vision

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MOT20: A benchmark for multi object tracking in crowded scenes

arXiv

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arXiv 2020年

作者： Dendorfer, Patrick Rezatofighi, Hamid Milan, Anton Shi, Javen Cremers, Daniel Reid, Ian Roth, Stefan Schindler, Konrad Leal-Taixé, Laura Dynamic Vision and Learning Group at TUM Munich Germany Australian Institute for Machine Learning School of Computer Science University of Adelaide Amazon Berlin Germany Photogrammetry and Remote Sensing Group ETH Zurich Switzerland Computer Vision Group at TUM Munich Germany Department of Computer Science Technische Universität Darmstadt Germany

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15 [5], MOT16 [8], and MOT17 [8] have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our MOT20 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark was presented first at the 4th BMTT MOT Challenge Workshop at the computer vision and Pattern Recognition Conference (CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for multiple object tracking when handling extremely crowded scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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Constrained parametric min-cuts for automatic object segmentation

Constrained parametric min-cuts for automatic object segment...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Joao Carreira Cristian Sminchisescu Computer Vision and Machine Learning Group Institute for Numerical Simulation Faculty of Mathematics and Natural Sciences University of Bonn Germany

We present a novel framework for generating and ranking plausible objects hypotheses in an image using bottom-up processes and mid-level cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge about properties of individual object classes, by solving a sequence of constrained parametric min-cut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict how plausible the segments are, given their mid-level region properties. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC09 segmentation dataset. It achieves the same average best segmentation covering as the best performing technique to date, 0.61 when using just the top 7 ranked segments, instead of the full hierarchy in. Our method achieves 0.78 average best covering using 154 segments. In a companion paper, we also show that the algorithm achieves state-of-the art results when used in a segmentation-based recognition pipeline.

关键词： Object segmentation Image segmentation Machine learning Art Humans Machine learning algorithms computer vision Numerical simulation Mathematics Predictive models

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Object recognition as ranking holistic figure-ground hypotheses

Object recognition as ranking holistic figure-ground hypothe...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Fuxin Li Joao Carreira Cristian Sminchisescu Computer Vision and Machine Learning Group Institute for Numerical Simulation Faculty of Mathematics and Natural Sciences University of Bonn Germany

We present an approach to visual object-class recognition and segmentation based on a pipeline that combines multiple, holistic figure-ground hypotheses generated in a bottom-up, object independent process. Decisions are performed based on continuous estimates of the spatial overlap between image segment hypotheses and each putative class. We differ from existing approaches not only in our seemingly unreasonable assumption that good object-level segments can be obtained in a feed-forward fashion, but also in framing recognition as a regression problem. Instead of focusing on a one-vs-all winning margin that can scramble ordering inside the non-maximum (non-winning) set, learning produces a globally consistent ranking with close ties to segment quality, hence to the extent entire object or part hypotheses spatially overlap with the ground truth. We demonstrate results beyond the current state of the art for image classification, object detection and semantic segmentation, in a number of challenging datasets including Caltech-101, ETHZ-Shape and PASCAL VOC 2009.

关键词： Object recognition Image segmentation Shape Machine learning Object detection Space exploration computer vision Numerical simulation Mathematics Pipelines

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CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking

CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Nicolas Baumann Michael Baumgartner Edoardo Ghignone Jonas Kühne Tobias Fischer Yung-Hsu Yang Marc Pollefeys Michele Magno Center for Project-Based Learning D-ITET ETH Zurich Computer Vision and Geometry Group D-INFK ETH Zurich

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in their cost-effectiveness. Notably, despite the prevalent use of Radio Detection and Ranging (RADAR) sensors in automotive systems, their potential in 3D detection and tracking has been largely disregarded due to data sparsity and measurement noise. As a recent development, the combination of RADARs and cameras is emerging as a promising solution. This paper presents Camera-RADAR 3D Detection and Tracking (CR3DT), a camera-RADAR fusion model for 3D object detection, and Multi-Object Tracking (MOT). Building upon the foundations of the State-of-the-Art (SotA) camera-only BEVDet architecture, CR3DT demonstrates substantial improvements in both detection and tracking capabilities, by incorporating the spatial and velocity information of the RADAR sensor. Experimental results demonstrate an absolute improvement in detection performance of 5.3% in mean Average Precision (mAP) and a 14.9% increase in Average Multi-Object Tracking Accuracy (AMOTA) on the nuScenes dataset when leveraging both modalities. CR3DT bridges the gap between high-performance and cost-effective perception systems in autonomous driving, by capitalizing on the ubiquitous presence of RADAR in automotive applications. The code is available at: https://***/ETH-PBL/CR3DT.

关键词： Solid modeling Meteorological radar Three-dimensional displays Accuracy Radar detection Radar Object detection Radar tracking Sensor systems Sensors

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LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels

LiDAR View Synthesis for Robust Vehicle Navigation Without E...

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International Conference on Intelligent Transportation

作者： Jonathan Schmidt Qadeer Khan Daniel Cremers Computer Vision Group School of Computation Information and Technology Technical University of Munich Munich Center for Machine Learning (MCML) University of Oxford

Deep learning models for self-driving cars require a diverse training dataset to manage critical driving scenarios on public roads safely. This includes having data from divergent trajectories, such as the oncoming traffic lane or sidewalks. Such data would be too dangerous to collect in the real world. Data augmentation approaches have been proposed to tackle this issue using RGB images. However, solutions based on LiDAR sensors are scarce. Therefore, we propose synthesizing additional LiDAR point clouds from novel viewpoints without physically driving at dangerous positions. The LiDAR view synthesis is done using mesh reconstruction and ray casting. We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output. A waypoint controller is then applied to this predicted trajectory to determine the throttle and steering labels of the ego-vehicle. Our method neither requires expert driving labels for the original nor the synthesized LiDAR sequence. Instead, we infer labels from LiDAR odometry. We demonstrate the effectiveness of our approach in a comprehensive online evaluation and with a comparison to concurrent work. Our results show the importance of synthesizing additional LiDAR point clouds, particularly in terms of model robustness. Code and supplementary visualizations are available at: https://***/lidar-synthesis/

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Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller

Multi Agent Navigation in Unconstrained Environments using a...

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International Conference on Intelligent Transportation

作者： Yining Ma Qadeer Khan Daniel Cremers Computer Vision Group School of Computation Information and Technology Technical University of Munich Munich Center for Machine Learning (MCML) University of Oxford

In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based Graphical Neural Network paradigm that takes into consideration the state of all the surrounding vehicles to make an informed decision. This allows each vehicle to smoothly reach its destination while also evading collision with the other agents. The data and corresponding labels for training such a network is obtained using an optimization based procedure. Experimental results demonstrate that our model is powerful enough to generalize even to situations with more vehicles than in the training data. Our method also outperforms comparable graphical neural network architectures. Project page which includes the code and supplementary information can be found here: https://***/multi-agent-control/

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Predicting the pathogenicity of protein coding mutations using Natural Language Processing

Predicting the pathogenicity of protein coding mutations usi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Naeem Rehmat Hammad Farooq Sanjay Kumar Sibt ul Hussain Hammad Naveed Computational Biology Research Lab (***) National University of Computer & Emerging Sciences Recognition Vision and Learning research group (ReVeaL) National University of Computer & Emerging Sciences

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that contribute to tumor growth from neutral ones is extremely challenging and is currently carried out manually. This manual annotation is very cumbersome and expensive in terms of time and money. In this study, we introduce a novel method "NLP-SNPPred" to read scientific literature and learn the implicit features that cause certain variations to be pathogenic. Precisely, our method ingests the bio-medical literature and produces its vector representation via exploiting state of the art NLP methods like sent2vec, word2vec and tf-idf. These representations are then fed to machine learning predictors to identify the pathogenic versus neutral variations. Our best model (NLPSNPPred) trained on OncoKB and evaluated on several publicly available benchmark datasets, outperformed state of the art function prediction methods. Our results show that NLP can be used effectively in predicting functional impact of protein coding variations with minimal complementary biological features. Moreover, encoding biological knowledge into the right representations, combined with machine learning methods can help in automating manual efforts. A free to use web-server is available at http://***.

关键词： Feature extraction Machine learning Natural language processing Training Cancer Tools Data models

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MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies

arXiv

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arXiv 2024年

作者： Yi, Jinhui Luo, Yanan Deichmann, Marion Schaaf, Gabriel Gall, Juergen Computer Vision Group University of Bonn Bonn Germany Plant Nutrition Group University of Bonn Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

An early, non-invasive, and on-site detection of nutrient deficiencies is critical to enable timely actions to prevent major losses of crops caused by lack of nutrients. While acquiring labeled data is very expensive, collecting images from multiple views of a crop is straightforward. Despite its relevance for practical applications, unsupervised domain adaptation where multiple views are available for the labeled source domain as well as the unlabeled target domain is an unexplored research area. In this work, we thus propose an approach that leverages multiple camera views in the source and target domain for unsupervised domain adaptation. We evaluate the proposed approach on two nutrient deficiency datasets. The proposed method achieves state-of-the-art results on both datasets compared to other unsupervised domain adaptation methods. The dataset and source code are available at https://***/jh-yi/MV-Match. Copyright © 2024, The Authors. All rights reserved.

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A systematic approach to constant power load stabilization by passive damping

A systematic approach to constant power load stabilization b...

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IEEE Annual Conference on Decision and Control

作者： J.C. Mayo-Maldonado P. Rapisarda Vision Learning and Control Group School of Electronics and Computer Science University of Southampton SO17 1BJ Southampton UK

ISBN: (纸本)9781479978878

We illustrate a novel design methodology for the stabilization of power converters with constant-power loads by passive damping. In our formulation of the problem we allow the destabilizing loads to be modeled by by frequency-dependent negative impedances. Our methodology is based on the solution of bilinear- and linear matrix inequalities, for which standard numerical solvers can be used.

关键词： constant power LOAD power conversion unit absorption of shock linear matrix inequalities Problem Formulation impedance

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