Human Body Contour Research Articles

The influence of car-seat design on its character experience

Producing higher efficiency cars with less and lighter materials but without compromising safety, comfort and driving pleasure might give a competitive advantage. In this light, at BMW a new light weight car-seat concept was developed based on the human body contour. A possibility to increase the comfort is using a seat which elicits positive tactile experiences. However, limited information is available on seat characteristics and tactile experiences. Therefore, this study describes the contour of three different car-seat designs, including a light weight seat, and the recorded corresponding emotion and tactile experience of 21 persons sitting in the seats. Results show that the new light weight car-seat concept rated well on experienced relaxedness, even with the lack of a side support. The most important findings are that hard seats with rather high side supports are rated sporty and seats that are softer are rated more luxurious.

A light weight car seat shaped by human body contour

The aim of this study is to develop a light-weight, comfortable seat. The idea is to shape a seat with a minimum of material by using the contour of the seated human. Twenty-five participants were asked to sit in a vacuum mattress placed on a wooden seat frame with similar angles as the car seat construction angles. They were instructed to sit in a comfortable position and perform driving movements. The mattress was then fixed, the contour scanned and digitised. All scans were superimposed giving input for a seat shell design made out of glass fibre laminate. The comfort experience of the shell was tested by 25 participants and compared with a standard BMW seat. The study shows that it is possible to create a rather comfortable seat using the human surface anatomy. However, more research on defining the specific form for the ideal shell is needed.

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.

Human motion tracking algorithm based on improved Snake model

An automatic human motion tracking algorithm combining improved Snake model and optical flow was proposed. The initial contour most close to human body was acquired by corner detection thus decreasing the iterations,and the probability of converging at local extreme value of Snake model was reduced. To resolve the problem that the tracking with Snake model is unstable and often loses the object,the strong feature points were chosen from the contour points in current frame for optical flow estimation,and then the result was chosen as the initial contour of next frame. Experimental results show that improved Snake model can make the initial coutour deform to the actual contour of human body,and realized the automatic and real-time human tracking.

Automated anthropometric data collection using 3D whole body scanners

The development of three-dimensional (3D) whole body scanner opens opportunities for measuring human body more efficiently. While taking measurements from 3D scanning data, markers are usually placed on human body to facilitate landmarking and data collection. But the procedure of placing markers is very tedious and may involve human errors. The objective of this study is to develop an automated anthropometric data collection system to eliminate manual intervention. For 3D image analysis, the first step is to segment the body parts by analyzing the 2D silhouette. Then we use the approximate location estimates as the references for performing initial searches of the landmarks. Subsequently, four algorithms including silhouette analysis, minimum circumference determination, gray-scale detection, and human-body contour plots are developed to extract 12 landmarks and three characteristic lines on human body. Finally, from the results of automated landmarking, 104 anthropometric data can be obtained. To evaluate the validity and reliability of the automated anthropometric data collection system, 189 human subjects were scanned and the scanning data were processed by the system. The results demonstrated that the system was very effective and robust. Based on the newly developed system, many applications can be extended.

Pedestrian registration in static images with unconstrained background

This paper introduces a human body contour registration method for static pedestrian images with unconstrained backgrounds. By using a statistical compound model to impose structural and textural constraints on valid pedestrian appearances, the matching process is robust to image clutter. Experimental results show that the proposed method register pedestrian contours in complex backgrounds effectively.