Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during ...the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusion. The goal of this survey article is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 260 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. A regularly updated project page is provided: https://github.com/zczcwh/DL-HPE.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
Automated human posture estimation (A-HPE) systems need delicate methods for detecting body parts and selecting cues based on marker-less sensors to effectively recognize complex activity motions. ...Recognition of human activities using vision sensors is a challenging issue due to variations in illumination conditions and complex movements during the monitoring of sports and fitness exercises. In this paper, we propose a novel A-HPE method that intelligently identifies human behaviours by utilizing saliency silhouette detection, robust body parts model and multidimensional cues from full-body silhouettes followed by an entropy Markov model. Initially, images are pre-processed and noise is removed to obtain a robust silhouette. Body parts models are then used to extract twelve key body parts. These key body parts are further optimized to assist the generation of multidimensional cues. These cues include energy, optical flow and distinctive values that are fed into quadratic discriminant analysis to discriminate cues which help in the recognition of actions. Finally, these optimized patterns are further processed by a maximum entropy Markov model as a recognizer engine based on transition and emission probability values for activity recognition. For evaluation, we used a leave-one-out cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving better body parts detection and higher recognition accuracy over four benchmark datasets. The proposed method will be useful for man-machine interactions such as 3D interactive games, virtual reality, service robots, e-health fitness, and security surveillance.
Graphical Abstract
Design model of automatic posture estimation and action recognition.
Full text
Available for:
CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a person's limbs and body parts even when the person ...is fully occluded from its sensor, and does so without placing any markers on the subject's body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.
Persons are often occluded by various obstacles in person retrieval scenarios. Previous person re-identification (re-id) methods, either overlook this issue or resolve it based on an extreme ...assumption. To alleviate the occlusion problem, we propose to detect the occluded regions, and explicitly exclude those regions during feature generation and matching. In this paper, we introduce a novel method named Pose-Guided Feature Alignment (PGFA), exploiting pose landmarks to disentangle the useful information from the occlusion noise. During the feature constructing stage, our method utilizes human landmarks to generate attention maps. The generated attention maps indicate if a specific body part is occluded and guide our model to attend to the non-occluded regions. During matching, we explicitly partition the global feature into parts and use the pose landmarks to indicate which partial features belonging to the target person. Only the visible regions are utilized for the retrieval. Besides, we construct a large-scale dataset for the Occluded Person Re-ID problem, namely Occluded-DukeMTMC, which is by far the largest dataset for the Occlusion Person Re-ID. Extensive experiments are conducted on our constructed occluded re-id dataset, two partial re-id datasets, and two commonly used holistic re-id datasets. Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.
The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a ...deep-learning-based method for predicting the positions of animal body parts. This framework consists of a graphical interface for labeling of body parts and training the network. LEAP offers fast prediction on new data, and training with as few as 100 frames results in 95% of peak performance. We validated LEAP using videos of freely behaving fruit flies and tracked 32 distinct points to describe the pose of the head, body, wings and legs, with an error rate of <3% of body length. We recapitulated reported findings on insect gait dynamics and demonstrated LEAP's applicability for unsupervised behavioral classification. Finally, we extended the method to more challenging imaging situations and videos of freely moving mice.
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which ...allows us to predict 2D and 3D poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classification-Regression architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests candidate poses at different locations in the image; 2) a classifier that scores the different pose proposals; and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses, which is shown to improve over a standard non maximum suppression algorithm. Our method recovers full-body 2D and 3D poses, hallucinating plausible body parts when the persons are partially occluded or truncated by the image boundary. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark and demonstrates satisfying 3D pose results even for multi-person images.
Zearalenone (ZEA) is a mycotoxin produced by the fungi of Fusarium genera, which contaminates the cereals and food stuffs worldwide. Fusarium mycotoxins are considered as important metabolites ...related to animal and human health. Evidences indicate that ZEA has been found to be present in different food stuffs from developed countries like USA, Canada, France, Germany, Japan, etc. and developing nations like Egypt, Thailand, Iran, Croatia, Philippines, etc. The toxicokinetic studies reveal that following oral exposure of ZEA, the compound is absorbed through gastrointestinal tract (GIT), gets metabolized and distributed to different body parts. ZEA has been shown to cause reproductive disorders in laboratory animals. Although the toxicity of ZEA in humans have not been conclusively established nonetheless, limited evidences indicate that ZEA can cause hyper estrogenic syndrome. Though, ZEA causes low acute toxicity, but reports are available confirming the systemic toxicity caused by ZEA. There is no review available that addresses the occurrence, systemic toxicity and the probable mechanisms of ZEA toxicity. This review shall address the world-wide occurrence and in vivo & in vitro toxicity studies of ZEA over the past 20 years. The review shall also discuss the toxicokinetics of ZEA and metabolites; illustrates the systemic toxicity and probable mechanisms of action leading to the risk associated with ZEA.
Full text
Available for:
BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
18.
PifPaf: Composite Fields for Human Pose Estimation Kreiss, Sven; Bertoni, Lorenzo; Alahi, Alexandre
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
2019-June
Conference Proceeding
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, ...uses a Part Intensity Field (PIF) to localize body parts and a Part Association Field (PAF) to associate body parts with each other to form full human poses. Our method outperforms previous methods at low resolution and in crowded, cluttered and occluded scenes thanks to (i) our new composite field PAF encoding fine-grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty. Our architecture is based on a fully convolutional, single-shot, box-free design. We perform on par with the existing state-of-the-art bottom-up method on the standard COCO keypoint task and produce state-of-the-art results on a modified COCO keypoint task for the transportation domain.
Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting ...involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.
PDF
