scholarly journals DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

2019 ◽  
Author(s):  
Jacob M. Graving ◽  
Daniel Chae ◽  
Hemal Naik ◽  
Liang Li ◽  
Benjamin Koger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Detection Algorithm ◽  
Estimation Methods ◽  
Body Parts ◽  
Software Toolkit ◽  
Multi Scale ◽  
Scientific Disciplines ◽  
Peak Detection Algorithm ◽  
Deep Learning Model

AbstractQuantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit,DeepPoseKit, that addresses these problems using an eZcient multi-scale deep-learning model, calledStacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2× with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

scholarly journals DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jacob M Graving ◽  
Daniel Chae ◽  
Hemal Naik ◽  
Liang Li ◽  
Benjamin Koger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Detection Algorithm ◽  
Estimation Methods ◽  
Body Parts ◽  
Software Toolkit ◽  
Multi Scale ◽  
Scientific Disciplines ◽  
Peak Detection Algorithm ◽  
Deep Learning Model

Quantitative behavioral measurements are important for answering questions across scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal’s body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings—including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

scholarly journals Attention-Guided Digital Adversarial Patches on Visual Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dapeng Lang ◽  
Deyun Chen ◽  
Ran Shi ◽  
Yongjun He
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Detection Algorithm ◽  
Classification Model ◽  
Practical Application ◽  
Single Target ◽  
Attachment Mechanism ◽  
Human Eyes ◽  
Deep Learning Model

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.

scholarly journals Deep Learning based Arrhythmia Classification with an ECG Acquisition System

2019 ◽  
Vol 9 (2) ◽  
pp. 3849-3852
Keyword(s):  
Deep Learning ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Data Set ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Peak Detection Algorithm ◽  
Different Types ◽  
Testing Accuracy

One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.

scholarly journals COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Anuprit Kale ◽  
Romain Paulus ◽  
Kazuma Hashimoto ◽  
Wenpeng Yin ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Question Answering ◽  
Health Workers ◽  
Complex Queries ◽  
Global Pandemic ◽  
Multi Stage ◽  
Scientific Disciplines ◽  
Abstractive Summarization ◽  
Deep Learning Model

AbstractThe COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question–answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system (http://einstein.ai/covid) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.

scholarly journals 3D Single Person Pose Estimation Method Based on Deep Learning

2020 ◽  
Author(s):  
Xinrui Yuan ◽  
Hairong Wang ◽  
Jun Wang
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Estimation Method ◽  
Hybrid Network ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Single Person ◽  
Depth Study ◽  
Human Pose ◽  
Set Up

In view of the significant effects of deep learning in graphics and image processing, research on human pose estimation methods using deep learning has attracted much attention, and many method models have been produced one after another. On the basis of tracking and in-depth study of domestic and foreign research results, this paper concentrates on 3D single person pose estimation methods, contrasts and analyzes three methods of end-to-end, staged and hybrid network models, and summarizes the characteristics of the methods. For evaluating method performance, set up an experimental environment, and utilize the Human3.6M data set to test several mainstream methods. The test results indicate that the hybrid network model method has a better performance in the field of human pose estimation.

scholarly journals Comparative Analysis of Deep Learning Methods for Detection of Keratoconus

2021 ◽  
Vol 23 (06) ◽  
pp. 1546-1553
Author(s):  
Impana N ◽  
◽  
K J Bhoomika ◽  
Suraksha S S ◽  
Karan Sawhney ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Eye Disease ◽  
Test Collection ◽  
Corneal Disease ◽  
Detection Model ◽  
Percent Accuracy ◽  
Deep Learning Model

Keratoconus eye disease is not an inflammatory corneal disease that is caused by progress in thinning of the cornea, scarring, and deformation in the shape of the cornea. In India, there is a significant increase in the number of cases of keratoconus, and several research centers have been paying attention to this disease in recent years. In this situation, there is an immediate need for tools that simplify both diagnosis and treatment[1]. The algorithm developed can decide whether the eye is a normal eye or keratoconus eye with stages. The K-net model analyzes the pentagram images of the eye using a convolutional neural network(CNN) a deep learning model and pre-trained ResNet-50 and InceptionV3 pre-trained models and does the comparative analysis of the accuracies of these models. The results show that the Keratoconus Detection algorithm leads to a good job, with a 93.75 percent accuracy on the data test collection. Keratoconus Detection model is a program that can help ophthalmologists test their patients faster, therefore reducing diagnostic errors and facilitating treatment.

scholarly journals Deep Learning Methods for 3D Human Pose Estimation under Different Supervision Paradigms: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2267
Author(s):  
Dejun Zhang ◽  
Yiqi Wu ◽  
Mingyue Guo ◽  
Yilin Chen
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
Literature Survey ◽  
Estimation Methods ◽  
Human Pose Estimation ◽  
Extensive Literature ◽  
Learning Methods ◽  
Human Pose ◽  
3D Human Pose Estimation ◽  
Research Studies

The rise of deep learning technology has broadly promoted the practical application of artificial intelligence in production and daily life. In computer vision, many human-centered applications, such as video surveillance, human-computer interaction, digital entertainment, etc., rely heavily on accurate and efficient human pose estimation techniques. Inspired by the remarkable achievements in learning-based 2D human pose estimation, numerous research studies are devoted to the topic of 3D human pose estimation via deep learning methods. Against this backdrop, this paper provides an extensive literature survey of recent literature about deep learning methods for 3D human pose estimation to display the development process of these research studies, track the latest research trends, and analyze the characteristics of devised types of methods. The literature is reviewed, along with the general pipeline of 3D human pose estimation, which consists of human body modeling, learning-based pose estimation, and regularization for refinement. Different from existing reviews of the same topic, this paper focus on deep learning-based methods. The learning-based pose estimation is discussed from two categories: single-person and multi-person. Each one is further categorized by data type to the image-based methods and the video-based methods. Moreover, due to the significance of data for learning-based methods, this paper surveys the 3D human pose estimation methods according to the taxonomy of supervision form. At last, this paper also enlists the current and widely used datasets and compares performances of reviewed methods. Based on this literature survey, it can be concluded that each branch of 3D human pose estimation starts with fully-supervised methods, and there is still much room for multi-person pose estimation based on other supervision methods from both image and video. Besides the significant development of 3D human pose estimation via deep learning, the inherent ambiguity and occlusion problems remain challenging issues that need to be better addressed.

scholarly journals Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2594 ◽  
Author(s):  
Delaram Jarchi ◽  
Javier Andreu-Perez ◽  
Mehrin Kiani ◽  
Oldrich Vysata ◽  
Jiri Kuchynka  ◽  
...  
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Sleep Disorders ◽  
Detection Algorithm ◽  
Learning Framework ◽  
Peak Detection Algorithm ◽  
Obstructive Sleep ◽  
Patient Groups ◽  
Synchrosqueezed Wavelet Transform ◽  
Pulse Peak

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

Sign in / Sign up

Export Citation Format

Share Document