scholarly journals Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
M. Thu ◽  
N. Suvonvorn

Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.

2015 ◽  
Vol 46 ◽  
pp. 45-52 ◽  
Author(s):  
Viswajith P. Viswanath ◽  
N.K. Ragesh ◽  
Madhu S. Nair

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.


2020 ◽  
Vol 11 (1) ◽  
pp. 305
Author(s):  
Rubén Escribano-García ◽  
Marina Corral-Bobadilla ◽  
Fátima Somovilla-Gómez ◽  
Rubén Lostado-Lorza ◽  
Ash Ahmed

The dimensions and weight of machines, structures, and components that need to be transported safely by road are growing constantly. One of the safest and most widely used transport systems on the road today due to their versatility and configuration are modular trailers. These trailers have hydraulic pendulum axles that are that are attached in pairs to the rigid platform above. In turn, these modular trailers are subject to limitations on the load that each axle carries, the tipping angle, and the oil pressure of the suspension system in order to guarantee safe transport by road. Optimizing the configuration of these modular trailers accurately and safely is a complex task. Factors to be considered include the load’s characteristics, the trailer’s mechanical properties, and road route conditions including the road’s slope and camber, precipitation and direction, and force of the wind. This paper presents a theoretical model that can be used for the optimal configuration of hydraulic cylinder suspension of special transport by road using modular trailers. It considers the previously mentioned factors and guarantees the safe stability of road transport. The proposed model was validated experimentally by placing a nacelle wind turbine at different points within a modular trailer. The weight of the wind turbine was 42,500 kg and its dimensions were 5133 × 2650 × 2975 mm. Once the proposed model was validated, an optimization algorithm was employed to find the optimal center of gravity for load, number of trailers, number of axles, oil pressures, and hydraulic configuration. The optimization algorithm was based on the iterative and automatic testing of the proposed model for different positions on the trailer and different hydraulic configurations. The optimization algorithm was tested with a cylindrical tank that weighed 108,500 kg and had dimensions of 19,500 × 3200 × 2500 mm. The results showed that the proposed model and optimization algorithm could safely optimize the configuration of the hydraulic suspension of modular trailers in special road transport, increase the accuracy and reliability of the calculation of the load configuration, save time, simplify the calculation process, and be easily implemented.


Author(s):  
Miloš Petković ◽  
Vladan Tubić ◽  
Nemanja Stepanović

Design hourly volume (DHV) represents one of the most significant parameters in the procedures of developing and evaluating road designs. DHV values can be accurately and precisely calculated only on the road sections with the implemented automatic traffic counters (ATCs) which constantly monitor the traffic volume. Unfortunately, many road sections do not contain ATCs primarily because of the implementation costs. Consequently, for many years, the DHV values have been defined on the basis of occasional counting and the factors related to traffic flow variability over time. However, it has been determined that this approach has significant limitations and that the predicted values considerably deviate from the actual values. Therefore, the main objective of this paper is to develop a model which will enable DHV prediction on rural roads in cases of insufficient data. The suggested model is based on the correlation between DHVs and the parameters defining the characteristics of traffic flows, that is, the relationship between the traffic volumes on design working days and non-working days, and annual average daily traffic. The results of the conducted research indicate that the application of the proposed model enables the prediction of DHV values with a significant level of data accuracy and reliability. The coefficient of determination (R2) shows that more than 98% of the variance of the calculated DHVs was explained by the observed DHV values, while the mean error ranged from 4.86% to 7.84% depending on the number of hours for which DHV was predicted.


Author(s):  
I.V. Balabin ◽  
O.I. Balabin ◽  
I.S. Chabunin

The article presents issues related to improving safety and efficiency of operation of mobile machines in the constantly changing, winter temperature and traffic conditions. The authors develop a conceptual model of winter all-weather tires able to adapt to various road conditions such as when the road is covered with a layer of ice or compacted snow, or when the road is free from snow and ice. The use of such winter all weather tires will improve the road safety by contributing to increasing the life of tires and preserving the road network. The proposed model has no foreign analogues and is protected by a patent of the Russian Federation.


Author(s):  
Jooin Lee ◽  
Hyeongcheol Lee

Intelligent Transportation System (ITS) is actively studied as the sensor and communication technology in the vehicle develops. The Intelligent Transportation System collects, processes, and provides information on the location, speed, and acceleration of the vehicles in the intersection. This paper proposes a fuel optimal route decision algorithm. The algorithm estimates traffic condition using information of vehicles acquired from several ITS intersections and determines the route that minimizes fuel consumption by reflecting the estimated traffic condition. Simplified fuel consumption models and road information (speed limit, average speed, etc.) are used to estimate the amount of fuel consumed when passing through the road. Dynamic Programming (DP) is used to determine the route that fuel consumption can be minimized. This algorithm has been verified in an intersection traffic model that reflects the actual traffic environment (Korea Daegu Technopolis) and the corresponding traffic model is modeled using AIMSUN.


2019 ◽  
Vol 9 (5) ◽  
pp. 996
Author(s):  
Fenglei Ren ◽  
Xin He ◽  
Zhonghui Wei ◽  
Lei Zhang ◽  
Jiawei He ◽  
...  

Road detection is a crucial research topic in computer vision, especially in the framework of autonomous driving and driver assistance. Moreover, it is an invaluable step for other tasks such as collision warning, vehicle detection, and pedestrian detection. Nevertheless, road detection remains challenging due to the presence of continuously changing backgrounds, varying illumination (shadows and highlights), variability of road appearance (size, shape, and color), and differently shaped objects (lane markings, vehicles, and pedestrians). In this paper, we propose an algorithm fusing appearance and prior cues for road detection. Firstly, input images are preprocessed by simple linear iterative clustering (SLIC), morphological processing, and illuminant invariant transformation to get superpixels and remove lane markings, shadows, and highlights. Then, we design a novel seed superpixels selection method and model appearance cues using the Gaussian mixture model with the selected seed superpixels. Next, we propose to construct a road geometric prior model offline, which can provide statistical descriptions and relevant information to infer the location of the road surface. Finally, a Bayesian framework is used to fuse appearance and prior cues. Experiments are carried out on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road benchmark where the proposed algorithm shows compelling performance and achieves state-of-the-art results among the model-based methods.


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