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GP22: A Car Styling Dataset for Automotive Designers

GP22: A Car Styling Dataset for Automotive Designers

引用

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

作者： Lee, Gyunpyo Kim, Taesu Suk, Hyeon-Jeong Korea Adv Inst Sci & Technol Dept Ind Design Color Lab Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance segmenting on car exterior exist, these large datasets are not relevant for design practices as the primary purpose lies in autonomous driving or vehicle verification. Therefore, we release GP22, composed of car styling features defined by automotive designers. The dataset contains 1480 car side profile images from 37 brands and ten car segments. It also contains annotations of design features that follow the taxonomy of the car exterior design features defined in the eye of the automotive designer. We trained the baseline model using YOLO v5 as the design feature detection model with the dataset. The presented model resulted in an mAP score of 0.995 and a recall of 0.984. Furthermore, exploration of the model performance on sketches and rendering images of the car side profile implies the scalability of the dataset for design purposes.

关键词： Image segmentation Annotations Feature detection Scalability Taxonomy Rendering (computer graphics) pattern recognition

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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture recognition 29

ChaLearn Looking at People RGB-D Isolated and Continuous Dat...

引用

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

作者： Wan, Jun Li, Stan Z. Zhao, Yibing Zhou, Shuai Guyon, Isabelle Escalera, Sergio Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Macau Univ Sci & Technol Macau Peoples R China UPSud Orsay France Univ Paris Saclay Paris France ChaLearn Barcelona Spain Univ Barcelona Comp Vis Ctr E-08007 Barcelona Spain

ISBN: (纸本)9781509014378

In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented.

关键词： Gesture recognition

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Box-Level Tube Tracking and Refinement for Vehicles Anomaly Detection

Box-Level Tube Tracking and Refinement for Vehicles Anomaly ...

引用

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

作者： Wu, Jie Wang, Xionghui Xiao, Xuefeng Wang, Yitong ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9781665448994

Traffic Anomaly detection is an essential computer vision task and plays a critical role in video structure analysis and urban traffic analysis. In this paper, we propose a box-level tracking and refinement algorithm to identify anomaly detection in road scenes. We first link the detection results to construct candidate spatio-temporal tubes via greedy search. Then the box-level refinement scheme is introduced to employ auxiliary detection cues to promote the abnormal predictions, which consists of spatial fusion, still-thing filter, temporal fusion, and feedforward optimization. Still-thing filter and feedforward optimization employ complementary detection concepts to promote the abnormal predictions, which helps determine an accurate abnormal period. The experimental results show that our approach is superior in the Traffic Anomaly Detection Track test set of the NVIDIA AI CITY 2021 CHALLENGE, which ranked second in this competition, with a 93.18% F1-score and 3.1623 root mean square error. It reveals that the proposed approach contributes to fine-grained anomaly detection in actual traffic accident scenarios and promoting the development of intelligent transportation.

关键词： computer vision Urban areas Transportation Electron tubes Artificial intelligence Vehicle dynamics Task analysis

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Concept Activation Vectors for Generating User-Defined 3D Shapes

Concept Activation Vectors for Generating User-Defined 3D Sh...

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

作者： Druc, Stefan Balu, Aditya Wooldridge, Peter Krishnamurthy, Adarsh Sarkar, Soumik MonolithAI 12-18 Hoxton St London N1 6NG England Iowa State Univ Ames IA 50011 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We explore the interpretability of 3D geometric deep learning models in the context of computer-Aided Design (CAD). The field of parametric CAD can be limited by the difficulty of expressing high-level design concepts in terms of a few numeric parameters. In this paper, we use a deep learning architectures to encode high dimensional 3D shapes into a vectorized latent representation that can be used to describe arbitrary concepts. Specifically, we train a simple auto-encoder to parameterize a dataset of complex shapes. To understand the latent encoded space, we use the idea of Concept Activation Vectors (CAV) to reinterpret the latent space in terms of user-defined concepts. This allows modification of a reference design to exhibit more or fewer characteristics of a chosen concept or group of concepts. We also test the statistical significance of the identified concepts and determine the sensitivity of a physical quantity of interest across the dataset.

关键词： Deep learning Solid modeling Three-dimensional displays Design automation Sensitivity Shape pattern recognition

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Efficient Conditional Pre-training for Transfer Learning

Efficient Conditional Pre-training for Transfer Learning

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

作者： Chakraborty, Shuvam Uzkent, Burak Ayush, Kumar Tanmay, Kumar Sheehan, Evan Ermon, Stefano Stanford Univ Stanford CA 94305 USA IIT Kharagpur Kharagpur W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

关键词： Training computer vision Costs Image resolution Filtering Computational modeling Transfer learning

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A Cost-effective Framework for Automated Vehicle-pedestrian Near-miss Detection through Onboard Monocular vision 30

A Cost-effective Framework for Automated Vehicle-pedestrian ...

引用

30th IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ke, Ruimin Lutin, Jerome Spears, Jerry Wang, Yinhai Univ Washington Seattle WA 98195 USA New Jersey Transit Newark NJ USA Washington State Transit Insurance Pool Olympia WA USA

ISBN: (纸本)9781538607336

Onboard monocular cameras have been widely deployed in both public transit and personal vehicles. Obtaining vehicle-pedestrian near-miss event data from onboard monocular vision systems may be cost-effective compared with onboard multiple-sensor systems or traffic surveillance videos. But extracting near-misses from onboard monocular vision is challenging and little work has been published. This paper fills the gap by developing a framework to automatically detect vehicle-pedestrian near-misses through onboard monocular vision. The proposed framework can estimate depth and real-world motion information through monocular vision with a moving video background. The experimental results based on processing over 30-hours video data demonstrate the ability of the system to capture near-misses by comparison with the events logged by the Rosco/MobilEye Shield+ system which includes four cameras working cooperatively. The detection overlap rate reaches over 90% with the thresholds properly set.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Strain Detection based on Breath and Motion Features Obtained by a Force Sensor for Smart Toilet Systems

Strain Detection based on Breath and Motion Features Obtaine...

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

作者： Akaho, Rina Yoshioka, Mototaka Panasonic Ind Corp Osaka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Aging people may be prone to accidents in bathrooms and toilets. The detection of strain motion for a smart toilet application has not been studied sufficiently. In this paper, we propose a method for strain detection from a force sensor placed on a toilet seat for a smart toilet healthcare application. The method first extracts breath and motion features that are assumed to be key components for the strain detection. The method then learns the discriminator model based on the random forest classifier using the aforementioned features. Finally, the method recognizes actions in the toilet room. There were five detection actions: seating, taking up toilet paper, wiping bottom, which are normal actions when sitting on a toilet seat, and strain actions (strong and weak). An experiment with 19 subjects was also conducted. Compared with a microwave sensor-based recognition, which is a conventional method (accuracy = 61.6%), our method was able to recognize the actions with high accuracy of 80.2% (significant test: T = 12.7, P < 0.01) in the experiment. Our strain detection method has the potential to be used as a smart toilet system to prevent blood pressure elevation and collapse caused by strain in the future.

关键词： conferences Windings Time series analysis Medical services Feature extraction Microwave theory and techniques pattern recognition

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Efficient GPU-based Graph Cuts for Stereo Matching

Efficient GPU-based Graph Cuts for Stereo Matching

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Choi, Young-kyu Park, In Kyu Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Inha Univ Sch Informat & Commun Engn Incheon South Korea

ISBN: (纸本)9780769549903

Although graph cuts (GC) is popularly used in many computer vision problems, slow execution time due to its high complexity hinders wide usage. Manycore solution using Graphics Processing Unit (GPU) may solve this problem. However, conventional GC implementation does not fully exploit GPU's computing power. To address this issue, a new GC algorithm which is suitable for GPU environment is presented in this paper. First, we present a novel graph construction method that accelerates the convergence speed of GC. Next, a repetitive block-based push and relabel method is used to increase the data transfer efficiency. Finally, we propose a low-overhead global relabeling algorithm to increase the GPU occupancy ratio. The experiments on Middlebury stereo dataset shows that 5.2X speedup can be achieved over the baseline implementation, with identical GPU platform and parameters.

关键词： computer vision graph theory graphics processing units image matching stereo image processing

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On-Orbit Inspection of an Unknown, Tumbling Target using NASA's Astrobee Robotic Free-Flyers

On-Orbit Inspection of an Unknown, Tumbling Target using NAS...

引用

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

作者： Oestreich, Charles Espinoza, Antonio Teran Todd, Jessica Albee, Keenan Linares, Richard MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Autonomous spacecraft critically depend on on-orbit inspection (i.e., relative navigation and inertial properties estimation) to intercept tumbling debris objects or defunct satellites. This work presents a practical method for on-orbit inspection and demonstrates its performance in simulation using NASA's Astrobee robotic free-flyers. The problem is formulated as a simultaneous localization and mapping task, utilizing IMU data from an observing "chaser" spacecraft and point clouds of the observed "target" spacecraft obtained via a 3D time-of-flight camera. The relative navigation between the chaser and target is solved via a factor graph-based approach. The target's principal axes of inertia are then estimated via a conic fit optimization procedure using a polhode analysis. Simulation results indicate the accuracy of the proposed method in preparation for hardware experiments on the International Space Station.

关键词： Space vehicles Three-dimensional displays Heuristic algorithms Robot vision systems Inspection Extraterrestrial measurements International Space Station

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WiFi and vision Multimodal Learning for Accurate and Robust Device -Free Human Activity recognition 32

WiFi and Vision Multimodal Learning for Accurate and Robust ...

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

作者： Zou, Han Yang, Jianfei Das, Hari Prasanna Liu, Huihan Zhou, Yuxun Spanos, Costas J. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781728125060

Human activity recognition plays an indispensable role in a myriad of emerging applications in context-aware services. Accurate activity recognition systems usually require the user to carry mobile or wearable devices, which is inconvenient for long term usage. In this paper, we design WiVi, a novel human activity recognition scheme that is able to identify common human activities in an accurate and device free manner via multimodal machine learning using only commercial WiFi-enabled IoT devices and camera. For sensing using WiFi, a new platform is developed to extract fine-grained WiFi channel information and transform them into WiFi frames. A tailored convolutional neural network model is designed to extract high-level representative features among the WiFi frames in order to provide human activity estimation. We utilized a variant of C3D model for activity sensing using vision. Following this, WiVi performs multimodal fusion at the decision level to combine the strength of WiFi and vision by constructing an ensembled DNN model. Extensive experiments are conducted in an indoor environment, demonstrating that WiVi achieves 97.5% activity recognition accuracy and is robust under unfavorable situations, as each modality provides the complementary sensing when the other faces its limiting conditions.

关键词： Information services

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