Silhouette Of Human Body Research Articles

Neural Basis of Categorical Representations of Animal Body Silhouettes.

Neural activities differentiating bodies versus non-body stimuli have been identified in the occipitotemporal cortex of both humans and nonhuman primates. However, the neural mechanisms of coding the similarity of different individuals' bodies of the same species to support their categorical representations remain unclear. Using electroencephalography (EEG) and magnetoencephalography (MEG), we investigated the temporal and spatial characteristics of neural processes shared by different individual body silhouettes of the same species by quantifying the repetition suppression of neural responses to human and animal (chimpanzee, dog, and bird) body silhouettes showing different postures. Our EEG results revealed significant repetition suppression of the amplitudes of early frontal/central activity at 180-220 ms (P2) and late occipitoparietal activity at 220-320 ms (P270) in response to animal (but not human) body silhouettes of the same species. Our MEG results further localized the repetition suppression effect related to animal body silhouettes in the left supramarginal gyrus and left frontal cortex at 200-440 ms after stimulus onset. Our findings suggest two neural processes that are involved in spontaneous categorical representations of animal body silhouettes as a cognitive basis of human-animal interactions.

A compact mathematical representation of human body silhouettes from frontal and lateral views

A compact mathematical representation of human body silhouettes from frontal and lateral views

A Compact Mathematical Representation of Human Body Silhouettes from Frontal and Lateral Views

A Compact Mathematical Representation of Human Body Silhouettes from Frontal and Lateral Views

Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling

Representation of spatio-temporal properties of human body silhouette and human-to-ground relationship, significantly contribute to the fall detection process. So, we propose an approach to efficiently model the spatio-temporal features using fall motion vector. First, we construct a Gaussian mixture model (GMM) called fall motion mixture model (FMMM) using histogram of optical flow and motion boundary histogram features to implicitly capture motion attributes in both the fall and non-fall videos. The FMMM contains both fall and non-fall attributes resulting in a high-dimensional representation. In order to extract only the relevant attributes for a particular fall or non-fall videos, we perform factor analysis on FMMM to get a low dimensional representation known as fall motion vector. Using fall motion vector, we are able to efficiently identify fall events in varieties of scenarios, such as the narrow angle camera (Le2i dataset), wide angle camera (URFall dataset), and multiple cameras (Montreal dataset). In all these scenarios, we show that the proposed fall motion vector achieves better performance than the existing methods.

A vision-based fall detection framework for the elderly in a room environment using motion features and DAG-SVM

Falls are one of the major health risks for the elderly in public health care issues, thus fall detection is particularly necessary for the seniors. In this paper, we propose a novel computer vision-based fall activity detection framework which is based on a new feature extraction method of human motion. We detect falls by analyzing the motion process of human body rather than conventional human shape analysis. Approximate ellipse is first adopted to fit human silhouette, and the areas outside the ellipse are removed to confirm human body silhouette (HBS). Then HBS is divided into three regions, and the motion features are extracted from the centroid coordinate changes of each region. A directed acyclic graph support vector machine (DAG-SVM) classifier is finally applied to detect falls from normal daily activities. The advantage of the proposed method is that it can not only effectively distinguish falls from fall-like activities but also detect the falls parallel to camera optical axis. Experimental results show that our method achieves a state-of-the-art accuracy in fall detection. This method is lightweight and thus can be integrated into embedded devices for real-time health surveillance.

A model-based gait recognition method with body pose and human prior knowledge

We propose in this paper a novel model-based gait recognition method, PoseGait. Gait recognition is a challenging and attractive task in biometrics. Early approaches to gait recognition were mainly appearance-based. The appearance-based features are usually extracted from human body silhouettes, which are easy to compute and have shown to be efficient for recognition tasks. Nevertheless silhouettes shape is not invariant to changes in clothing, and can be subject to drastic variations, due to illumination changes or other external factors. An alternative to silhouette-based features are model-based features. However, they are very challenging to acquire especially for low image resolution. In contrast to previous approaches, our model PoseGait exploits human 3D pose estimated from images by Convolutional Neural Network as the input feature for gait recognition. The 3D pose, defined by the 3D coordinates of joints of the human body, is invariant to view changes and other external factors of variation. We design spatio-temporal features from the 3D pose to improve the recognition rate. Our method is evaluated on two large datasets, CASIA B and CASIA E. The experimental results show that the proposed method can achieve state-of-the-art performance and is robust to view and clothing variations.

Spatiotemporal features of human motion for gait recognition

Gait is a novel biometric feature that offers human identification at a distance and without physical interaction with the imaging device. Moreover, it performs well even in low resolution which makes it ideal for use in numerous human identification applications, e.g.,visual surveillance, monitoring and access control systems. Most existing gait-based human identification solutions extract human body silhouettes, contours or shapes from the images and construct gait features. Therefore, the performance of such algorithms highly depends upon the accuracy of human body segmentation, which is still a challenging problem in the literature. In this paper, we propose a new gait recognition algorithm which uses the spatial and temporal motion characteristics of human gait for individual identification without needing the silhouette extraction. The proposed algorithm extracts a set of spatiotemporal local descriptors from the gait video sequences. The extracted descriptors are encoded using the Fisher vector encoding and Gaussian mixture model-based codebook. The encoded features are classified using a simple linear support vector machine to recognize the individuals. The proposed gait recognition method is evaluated on five widely used gait databases, including indoor (CMU MoBo, CASIA-B) and outdoor (NLPR, CASIA-C, TUM GAID) gait databases. The results reveal that our method showed excellent performance on all five databases and outperformed the state-of-the-art gait recognition approaches.

When a silhouette appears male: Observer’s own physical fitness governs social categorization of sexually ambiguous stimuli

Categorization bias in social cognition is widely observed, as it is more beneficial to commit low-risk biases in order to reduce high-risk biases. Here, we hypothesized that a low capacity of self-protection leads to a biased social categorization of others as “harmful”. To examine this, silhouettes of human body, which had a sexually dimorphic cue (waist-to-hip ratio), were presented, and participants categorized the stimuli as being either “female” or “male”. Participants’ basic physical fitness was measured, and we manipulated exogenous physical load by either a heavy or a light backpack. Physical load did not significantly influence sex categorization. In contrast, only female participants tended to categorize the stimuli as “male” more, as basic physical fitness increased, suggesting that basic physical fitness, not physical load, affects sex categorization for females. Our findings shed new light on the role of fitness factors in social cognition.

Recognition and Classification of Human Behavior in Intelligent Surveillance Systems using Hidden Markov Model

Nowadays, the human behavior analysis by computer vision techniques has been an interesting issue for researchers. Automatic recognition of actions in video allows automation of many otherwise manually intensive tasks such as video surveillance. Video surveillance system especially for elderly care and their behavior analysis has an important role to take care of aged, impatient or bedridden persons. In this paper, we propose a high accuracy human action classification and recognition method using hidden Markov model classifier. In our approach, first, we use star skeleton feature extraction method to extract extremities of human body silhouette to produce feature vectors as inputs of hidden Markov model classifier. Then, hidden Markov model, which is learned and used in our proposed surveillance system, classifies the investigated behaviors and detects abnormal actions with high accuracy in comparison by other abnormal detection reported in previous works. The accuracy about 94% resulted from confusion matrix approve the efficiency of the proposed method when compared with its counterparts for abnormal action detection.

3차원 인체스캐너를 이용한 거들 착용에 따른 인체 형상 단면도와 실루엣 변화 분석

【The aim of this study was to use a 3D human body scanner to analyze the cross section of different body parts when a girdle is worn. Two types of girdles were selected as experimental garments: a standard type girdle (Garment A) and a high-waist type girdle (Garment B). Their sizes were 88 (S) and 94 (M). Ten female subjects in their twenties who wear girdles sizes 88 (S) and 94 (M) participated the experiment. Their bodies were scanned three times with the 3D human body scanner, before and after wearing experimental girdles. The data were collected by overlapping the cross sections of the 3D scanned body shape data. The space length was measured from the overlapped cross sections. The results show that human body silhouette are changed after wearing the compression type garments and the amount and place of the body change is different by style of garments. First, the waist girth shape became rounder. Second, there was a definite difference in space amount at abdomen girth between two types of girdle. The abdomen area was pushed toward the front after wearing the standard type girdle (A). The high-waist type girdle (B) pushed abdomen area toward the back. Third, there was clear difference at the hip area after wearing two types of girdle. The hip area pushed toward the front with the standard type girdle (A) and pushed toward the back with the high-waist type girdle (B).】

Depth-images-based pose estimation using regression forests and graphical models

Depth-images-based human pose estimation is facing two challenges: how to extract features which are discriminative to variations in human poses and robust against noise, and how to reliably learn body joints based on their dependence structure. To tackle the first problem, we propose a novel 3D Local Shape Context feature extracted from human body silhouette to characterise the local structure of body joints. To tackle the second problem, we incorporate a graphical model into regression forests to exploit structural constrains. Experiments demonstrate that our method can efficiently learn local body structures and localise joints. Compared with the state-of-the-art methods, our method significantly improves the accuracy of pose estimation from depth images.

Multi-View Human Action Recognition Using Wavelet Data Reduction and Multi-Class Classification

Human action recognition from video has several potential to apply in different real-life applications, but the most cases in this field suffer from the variation in viewpoint. Most of published methods in this area are considered the performance of each single camera, therefore the change in the viewpoints significantly decrease the recognition rate. In this paper, multiple views are considered together and a method has proposed to recognize human action depicted in multi-view image sequences. In the first step, the border of the human body's silhouette is extracted and distance signal is calculated. In the next step, the wavelet transform is applied to extract coefficients of single-view features, and then the extracted features are combined to compose multi-view features. Finally a hierarchical classifier using support vector machine and Naive Bayes classifiers is implemented to classify the actions. The average of overall action recognition accuracy for 12 actions using 5 different angles of views on the IXMAS dataset is 88.22. The results of experiments on the popular multi-view dataset have shown the proposed method achieves high and state-of-the-art success rates. In other word, combination of single-view extracted features from the wavelet approximation coefficients and composing the multi-view features can be used as the multi-view features. Further, the hierarchical classifier can be applied to recognize actions in multi-view human action recognition area.

An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.

An Indoor Human Activity Recognition System for Smart Home Using Local Binary Pattern Features with Hidden Markov Models

Smart home technologies are getting considerable attentions nowadays for better care of the residents, especially the elderly. One of the key technologies is the human activity recognition (HAR) system which automatically recognizes various indoor activities of a resident and reacts upon the needs of the resident, known as a proactive system. In this work, we propose a novel HAR system that utilizes depth imaging. Our HAR system utilizes local binary patterns (LBP) as local activity features from depth silhouettes and recognizes human activities via Hidden Markov Model (HMM). In our methodology, first LBP features were extracted from depth human body silhouettes from each frame of a video containing human activity. Then, principal component analysis (PCA) and linear discriminant analysis (LDA) were performed over the LBP features to obtain condensed features. Applying these features, each activity HMM was trained. Finally, HAR was performed with the trained HMMs. Our approach shows superior recognition performance over the traditional silhouette feature-based approaches. The system should be practical to be used for smart homes.

Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home

Video-based human activity recognition systems have potential contributions to various applications such as smart homes and healthcare services. In this work, we present a novel depth video-based translation and scaling invariant human activity recognition (HAR) system utilizing R transformation of depth silhouettes. To perform HAR in indoor settings, an invariant HAR method is critical to freely perform activities anywhere in a camera view without translation and scaling problems of human body silhouettes. We obtain such invariant features via R transformation on depth silhouettes. Furthermore, in R transforming depth silhouettes, shape information of human body reflected in depth values is encoded into the features. In R transformation, 2D feature maps are computed first through Radon transform of each depth silhouette followed by computing 1D feature profile through R transform to get the translation and scaling invariant features. Then, we apply Principle Component Analysis (PCA) for dimension reduction and Linear Discriminant Analysis (LDA) to make the features more prominent, compact and robust. Finally, Hidden Markov Models (HMMs) are used to train and recognize different human activities. Our proposed system shows superior recognition rate over the conventional approaches, reaching up to the mean recognition rate of 93.16% for six typical human activities whereas the conventional PC and IC-based depth silhouettes achieved only 74.83% and 86.33% ,while binary silhouettes-based R transformation approach achieved 67.08% respectively. Our experimental results show that the proposed method is robust, reliable, and efficient in recognizing the daily human activities.

Constructing 3D human model from front and side images

Constructing 3D human model from 2D images provides a cost-effective approach to visualize digital human in virtual environment. This paper presents a systematic approach for constructing 3D human model using the front and side images of a person. The silhouettes of human body are first detected and the feature points on the silhouettes are subsequently identified. The feature points are further used to obtain the body dimensions that are necessary for identifying a template 3D human model. The shape of the template human model can be modified by the free-form deformation method. Moreover, the proposed approach has been applied for constructing the 3D human models of 30 subjects. The comparisons between the constructed 3D models and the 3D scanning models of the 30 subjects indicate that the proposed system is very effective and robust.

Automated body feature extraction from 2D images

Extracting human body features automatically from 2D images provides a fast and easy approach to collect anthropometric data. This paper presents a systematic method to detect feature points on the silhouette of human body from the front and side images. With an efficient shape coding algorithm, the human body contour of the binary images can be represented. By evaluating the difference between the coding sequence, feature points can be identified. Hence, a total of 60 feature points can be extracted automatically. The method has been tested on 30 human subjects and all the feature points can be correctly extracted. In order to evaluate the performance of the automatic body feature extraction system, the feature points obtained from the proposed method were validated by analyzing the location variation of the silhouette curve. The experimental results indicate that the system is very effective and robust. Moreover, the extracted feature points can be subsequently processed for body dimension measurements. Thus, the newly developed system can achieve an automated extraction of body features and to obtain anthropometric data for many applications.

A fall detection system using k-nearest neighbor classifier

The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection.

Computer Vision-Based Human Body Segmentation and Posture Estimation

This paper proposes a new method for vision-based human body posture estimation using body silhouette and skin-color information. A moving object segmentation algorithm is first proposed to distinguish the human body from the background using a sequence of images. This algorithm uses a fast Euler number computation technique to automatically determine the threshold of both frame and background differences. After segmentation, a sequence of image processing approaches then creates a complete silhouette of the human body. The objective of posture estimation is to locate five significant body points, including the head, tips of the feet, and tips of the hands. These significant points are first selected from convex points on a defined distance curve. A number of heuristic rules based on body shape characteristics are used to select the proper points among these convex candidates. These rules use features like the principal and minor axes of the human body, their interactions with the silhouette contour, the relative distances between convex points, and the curvature of convex points. An auxiliary skin-color feature is used when the silhouette shape features alone are not sufficient to estimate the significant points. Experimental results show that the proposed approach can efficiently and effectively locate the significant body points for most postures.

휴먼-로봇 인터액션을 위한 하이브리드 스켈레톤 특징점 추출

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.