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Multiple People Tracking using Body and Joint Detections 32

Multiple People Tracking using Body and Joint Detections

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Henschel, Roberto Zou, Yunzhe Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany

ISBN: (纸本)9781728125060

Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However, despite an enormous progress in recent years, partially occluded people are still often not recognized. Also, many correct detections are mistakenly discarded when the non-maximum suppression is performed. Improving the tracking performance thus requires to augment the coarse input. Well suited for this task are fine-graded body joint detections, as they allow to locate even strongly occluded persons. Thus in this work, we analyze the suitability of including joint detections for multiple people tracking. We introduce different affinities between the two detection types and evaluate their performances. Tracking is then performed within a near-online framework based on a Min cost graph labeling formulation. As a result, our framework can recover heavily occluded persons and solve the data association efficiently. We evaluate our framework on the MOT16/17 benchmark. Experimental results demonstrate that our framework achieves state-of-the-art results.

关键词： computer vision

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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Locally-Weighted Elastic Comparison of Planar Shapes 31

Locally-Weighted Elastic Comparison of Planar Shapes

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Strait, Justin Kurtek, Sebastian MacEachern, Steven Ohio State Univ Dept Stat Columbus OH 43210 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Registration of curves is a necessary component of statistical shape analysis. The goal of registration is to align collections of shapes so that common features are appropriately matched for further comparison and subsequent analyses. Traditional methods for registration typically rely on optimizing an energy functional over a set of appropriate shape-preserving transformations (i.e., rotations and re-parameterizations). These functionals typically rely on the standard L-2 metric. In certain applications, it may make sense to use a more flexible metric which can align shapes most preferably with respect to a local shape feature (i.e., a certain curve segment selected from the overall shape). In this work, we define a weighted shape metric which allows for emphasis on local shape features. Registration can be performed with respect to this metric. We demonstrate the registration procedure using simulated curves as well as real data, and show the dependence of the optimal rotation and re-parameterization on the specified weights, as well as the resulting deformation path from one shape to another.

关键词： Shape Measurement Silicon computer vision Standards Strain Beak

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Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions 29

Extended DISFA Dataset: Investigating Posed and Spontaneous ...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Mavadati, Mohammad Sanger, Peyten Mahoor, Mohammad H. Univ Denver Dept Elect & Comp Engn 2390 S York St Denver CO 80208 USA

ISBN: (纸本)9781509014378

Automatic facial expression recognition (FER) is an important component of affect-aware technologies. Because of the lack of labeled spontaneous data, majority of existing automated FER systems were trained on posed facial expressions;however in real-world applications we deal with (subtle) spontaneous facial expression. This paper introduces an extension of DISFA, a previously released and well-accepted face dataset. Extended DISFA (DISFA+) has the following features: 1) it contains a large set of posed and spontaneous facial expressions data for a same group of individuals, 2) it provides the manually labeled frame-based annotations of 5-level intensity of twelve FACS facial actions, 3) it provides meta data (i.e. facial landmark points in addition to the self-report of each individual regarding every posed facial expression). This paper introduces and employs DISFA+, to analyze and compare temporal patterns and dynamic characteristics of posed and spontaneous facial expressions.

关键词： computer keyboards

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Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Video Class Agnostic Segmentation Benchmark for Autonomous D...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England

ISBN: (纸本)9781665448994

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://***/vca/.

关键词： Training computer vision Tracking Motion segmentation Semantics Video sequences Benchmark testing

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Geometry-aware Traffic Flow Analysis by Detection and Tracking 31

Geometry-aware Traffic Flow Analysis by Detection and Tracki...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shi, Honghui Wang, Zhonghao Zhang, Yang Wang, Xinchao Huang, Thomas UIUC IFP Grp Beckman Inst Champaign IL 61820 USA IBM Res San Jose CA 95120 USA Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In the second Nvidia AI City Challenge hosted in 2018, the traffic flow analysis challenge proposes an interest task that requires participants to predict the speed of vehicles on road from various traffic camera videos. We propose a simple yet effective method combing both learning based detection and geometric calibration based estimation. We use a learning based method to detect and track vehicles, and use a geometry based camera calibration method to calculate the speed of those vehicles. We achieve a perfect detection rate of target vehicles and a root mean square error (RMSE) of 6.6674 in predicting the vehicle speed, which rank us the third place in the competition.

关键词： Videos Roads Cameras Estimation Tracking computer vision Artificial intelligence

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Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

Inaccuracy of State-Action Value Function For Non-Optimal Ac...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665448994

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.

关键词： Training Resistance Deep learning computer vision Systematics Perturbation methods conferences

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Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Searching for Efficient Neural Architectures for On-Device M...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akin, Berkin Gupta, Suyog Long, Yun Spiridonov, Anton Wang, Zhuo White, Marie Xu, Hao Zhou, Ping Zhou, Yanqi

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures1 that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

关键词： computer vision Tensors Costs Convolution Image edge detection computer architecture Throughput

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Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

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Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

Reasoning with Multi-Structure Commonsense Knowledge in Visu...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Shunyu Jiang, Xiaoze Yang, Zequn Wan, Tao Qin, Zengchang Beihang Univ Sch ASEE Intelligent Comp & Machine Learning Lab Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch BSME Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the content of an image, while a considerable amount of commonsense-required questions are ignored. Handling these scenarios depends on logical reasoning that requires commonsense priors. How to capture relevant commonsense knowledge complementary to the history and the image remains a key challenge. In this paper, we propose a novel model by Reasoning with Multi-structure Commonsense Knowledge (RMK). In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image. On top of these multi-structure representations, our model can capture relevant knowledge and incorporate them into the vision and semantic features, via graph-based interaction and transformer-based fusion. Experimental results and analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model effectively outperforms comparative methods.

关键词： Visualization Fuses Semantics Knowledge based systems Oral communication Transformers pattern recognition

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