Truck body type classification using a deep representation learning ensemble on 3D point sets

2021 ◽  
Vol 133 ◽  
pp. 103461
Author(s):  
Yiqiao Li ◽  
Koti Reddy Allu ◽  
Zhe Sun ◽  
Andre Y.C. Tok ◽  
Guoliang Feng ◽  
...  
Keyword(s):  
Representation Learning ◽  
Point Sets ◽  
Body Type ◽  
Type Classification

scholarly journals Truck Body-Type Classification using Single-Beam Lidar Sensors

2019 ◽  
Vol 2673 (1) ◽  
pp. 26-40 ◽  
Author(s):  
Magdalena I. Asborno ◽  
Collin G. Burris ◽  
Sarah Hernandez
Keyword(s):  
Model Development ◽  
True Positive Rate ◽  
Classification Model ◽  
Proof Of Concept ◽  
Body Type ◽  
Commodity Flow ◽  
Single Beam ◽  
Loop Detectors ◽  
Low Profile ◽  
Type Classification

Understanding commodity flow through a region is key for estimating the demand for freight transportation facilities and services, forecasting energy consumption, analyzing safety risks, and addressing environmental concerns. Transportation planners and decision makers use commodity flow data to develop and implement long-term freight plans and manage infrastructure. State-of-the-practice commodity flow estimations based on regional socioeconomic data and periodic surveys have limited spatial and temporal coverage. Moreover, no existing methods tie vehicles to commodity movements at the link level. Although intrusive inductive loop detectors can identify the industry served (or commodity carried) by trucks based on the truck’s body type, intrusive sensor performance is limited by pavement quality. Unfortunately, poor pavement conditions are common in locations with high truck volumes. This paper investigates the use of a non-intrusive traffic sensor, Lidar, for high-resolution truck body-type classification. This paper develops a proof-of-concept Lidar sensor and a truck body-type classification model capable of classifying five-axle tractor-trailers into distinct body types: van and container, platform, low-profile trailer, tank, and hopper and end dump. These body-class groups link to commodity movements and provide insight into link-level commodity flows. Data for model development and validation were collected along a major interstate corridor and a low-speed local road. The classification model achieves an 81% true positive rate (TPR) with class-specific TPR as high as 94% and average volume accuracy of 87% for the primary test location. Overall, the proposed sensor represents an adequate proof of concept to evaluate the industry served by trucks on a network link.

Upper Body Type Classification of Elementary School Boys Using 3D Data

2019 ◽  
Vol 21 (6) ◽  
pp. 789-799
Author(s):  
Hyun Wook Kim ◽  
Yun Ja Nam
Keyword(s):  
Elementary School ◽  
Upper Body ◽  
Body Type ◽  
3D Data ◽  
Type Classification

scholarly journals Ájurvéda az elhízás kezelésében

2016 ◽  
Vol 157 (34) ◽  
pp. 1349-1352
Author(s):  
Anna Korossy ◽  
Anna Blázovics
Keyword(s):  
Cardiovascular Diseases ◽  
Fast Food ◽  
Lifestyle Changes ◽  
The Body ◽  
Stress Factors ◽  
Food Chains ◽  
Body Type ◽  
Body Constitution ◽  
And Oxidative Stress ◽  
Type Classification

Obesity is an increasing problem all over the world as the lifestyle changes and fast food chains gain popularity. In India, 31% of men and 29% of women are overweight, which is a growing trend over the last 11 years. Obesity increases the risk of many diseases such as cardiovascular diseases, reflux disease, gastrointestinal tumors, and sleep apnea. Obesity without complications can also cause serious complications during surgery. In Ayurveda the formation of diseases depends on the balance of the three doshas – vata, pitta, kapha. The rate of three doshas varies depending on the body constitution of the indvidual. Studies of an Indian research group have shown that Ayurvedic body type classification may be associated with genes of inflammation and oxidative stress factors, the rate of DNA methylation and development of cardiovascular diseases. Orv. Hetil., 2016, 157(34), 1349–1352.

Body Type Classification and Increment Analysis

2011 ◽  
Vol 317-319 ◽  
pp. 1872-1875
Author(s):  
Xiao Ning Jing ◽  
Xiao Jiu Li
Keyword(s):  
Mass Production ◽  
Young Female ◽  
The Body ◽  
Female College Students ◽  
Intermediate Type ◽  
Body Type ◽  
Pattern Grading ◽  
The Social ◽  
Type Classification ◽  
Size Data

With the social mass production of garment industry, the same style clothing requires organizing production in various specifications or size series to meet the different needs of consumers. Fast, accurate and flexible automatic grading technology has become the research hotspot, and the scientific size table is the basis of this technology. This study collects the size data of 251 female college students. Then make the body type classification and gain the size table of intermediate type and the increment of each body type. This study provide the basis for MTM automatically parameter table of young female university students to establish a pattern grading system.

Somatotype identification of middle-aged women based on decision tree algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lanmin Wang ◽  
Hongmin Wang ◽  
Huiyan Zhang ◽  
Naiseman Akemujiang ◽  
Aimin Xiao
Keyword(s):  
Decision Tree ◽  
Body Shape ◽  
Great Influence ◽  
The Body ◽  
Decision Tree Algorithm ◽  
Middle Aged ◽  
Body Type ◽  
Tree Algorithm ◽  
Content Type ◽  
Type Classification

PurposeBody type classification has a great influence on plate making and garment sizing system, and the accuracy of body type classification method will greatly affect the fit of garment production. The purpose of this paper is to use the decision tree algorithm to study body classification rules, develop a decision tree body recognition model and judge the body shape of middle-aged women in Xinjiang.Design/methodology/approachFirst, perform dimensionless processing on the collected data of 256 middle-aged women in Xinjiang, and the dimensionless data were used for K-means body clustering; Then, quantitatively analyze the effectiveness of different classification clusters based on the silhouette coefficients. Second, the decision tree algorithm is used to divide the classified sample data into a training set and a test set at a ratio of 70/30, and select the best node and the best branch based on the Gini coefficient to construct a classification tree. Last, the overall optimal decision tree is generated by means of hyperparameter pruning.FindingsThe body shape of middle-aged women in Xinjiang can be divided into three types: standard body, plump body and obese body. The decision tree model has an excellent effect on body classification of middle-aged women in Xinjiang (precision (macro), 95.46%; precision (micro), 95.95%; recall (macro), 95.46%; recall (micro), 95.95%; F1 (macro), 95.46%; F1 (micro), 95.95%).Originality/valueFor scientific research, this paper is conducive to increasing the regional body type theory and stimulating the establishment of a garment sizing subdivision system in Xinjiang. In terms of production practice, this paper not only establishes a model for judging the shape of middle-aged women in Xinjiang, but also provides reference data for intermediates of various sizes. In addition, to facilitate pattern-making and the establishment of a subdivision system for the size of middle-aged women's garments in Xinjiang, this paper provides the grading values of various body control parts of middle-aged women in Xinjiang.

Deep Representation Learning for Orca Call Type Classification

2019 ◽  
pp. 274-286
Author(s):  
Christian Bergler ◽  
Manuel Schmitt ◽  
Rachael Xi Cheng ◽  
Hendrik Schröter ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Representation Learning ◽  
Call Type ◽  
Type Classification

scholarly journals Graph Representation Learning for Road Type Classification

2021 ◽  
pp. 108174
Author(s):  
Zahra Gharaee ◽  
Shreyas Kowshik ◽  
Oliver Stromann ◽  
Michael Felsberg
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Road Type ◽  
Type Classification
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