Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors

Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction

This paper has been presented at 8th International Conference of Pattern Recognition Systems.This paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. The extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. The obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santande

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524) pp 139-148 Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11524)Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11524)This research describes “EspiNet”, a Deep Learning Convolutional Neural Network model, in conjunction with a Markov Decision Process (MDP) tracker for detection and tracking of occluded motorcycles in urban environments. The model is trained and evaluated, using a new public dataset with up to 10,000 annotated images, created for this research, and captured in real urban traffic scenes. Images were captured using a moving camera mounted in a drone, where more than 60% of the motorcycles are affected by occlusions. The network design involves many tests, where a promising result of 88.84% in average precision (AP) is achieved, despite the considerable number of occluded vehicles, the movement of the camera and the low angle used for capture. The model predictions are used as input to an MDP tracker, reaching results up to 85.2% in Multiple Object Tracking Accuracy (MOTA). The proposed network architecture outperforms state of the art YOLO (You Look Only Once) v3.0 and Faster R-CNN (VGG16 based) detection models, producing also better tracking results in comparison with the use of the other two models as detector base for the MDP tracker.This work was partially supported by COLCIENCIAS project: Reduccion de Emisiones Vehiculares Mediante el Modelado y Gestion Optima de Trafico en Areas Metropolitanas - Caso Medellin - Area Metropolitana del Valle de Aburra, codigo 111874558167, CT 049-2017. Universidad Nacional de Colombia. Proyecto HERMES 25374. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research

Nanocellulose from Spanish Harvesting Residues to Improve the Sustainability and Functionality of Linerboard Recycling Processes

The hornification processes undergone by the fibers in the paper industry recycling processes lead to the loss of properties of the final products, which exhibit poor mechanical properties. Among the most promising solutions is the reinforcement of secondary fibers with cellulose nanofibers. The present work addresses two important issues: the efficient production of cellulose nanofibers from scarcely exploited agricultural wastes such as horticultural residues and vine shoots, and their application as a reinforcement agent in recycled linerboard recycling processes. The effect of the chemical composition and the pretreatment used on the nanofibrillation efficiency of the fibers was analyzed. Chemical pretreatment allowed a significantly higher nanofibrillated fraction (45–63%) than that produced by mechanical (18–38%), as well as higher specific surface areas (>430 m2/g). The application of the nanofibers as a reinforcing agent in the recycled linerboard considerably improved the mechanical properties (improvements of 15% for breaking length, 220–240% for Young’s modulus and 27% for tear index), counteracting the loss of mechanical properties suffered during recycling when using chemically pretreated cellulose nanofibers from horticultural residues and vine shoots. It was concluded that this technology surpasses the mechanical reinforcement produced by conventional mechanical refining used in the industry and extends the number of recycling cycles of the products due to the non-physical modification of the fibers

An Exploratory Study of Social and Personal Identity of Selected Anglo-American, Mexican-American, and Mexican National College Students

Social and personal identities were examined for samples of college graduate students in three culturally different groups: Anglo-American, Mexican-American, and Mexican National. Four identities were explored in terms of salience, frequency, and valence: religious, family, gender, and peer. No differences were found between the groups on religious and gender identities. As predicted, both the Mexican National and Mexican-American groups scored family higher than did members of the Anglo- American group. Peer responses were not analyzed because there was insufficient data. Three incidental questions were also explored; the salience of consensual versus subconsensual identities; sex difference regarding family and gender identities; and declared religious faith and family and gender identities. For all three cultural groups, and for both sexes, family emerged as the most prominent identity. Gender is the second most important identity, followed by religious and peer identities respectively. Of the incidental questions, the most important finding was that whereas most of the previous research using the same instrument found consensual identities predominant, subconsensual responses were predominant in this study. There were no male-female differences either within or across groups. Such findings raised a question about whether previous cross-cultural studies of sex role could, in fact, comment meaningfully on cultural differences. This was an exploratory study using a small number of subjects (seven men and women in each group) which were not randomly selected. Therefore, the findings were severly qualified

Detecting, tracking and counting people getting on/off a metropolitan train using a standard video camera

The main source of delays in public transport systems (buses, trams, metros, railways) takes place in their stations. For example, a public transport vehicle can travel at 60 km per hour between stations, but its commercial speed (average en-route speed, including any intermediate delay) does not reach more than half of that value. Therefore, the problem that public transport operators must solve is how to reduce the delay in stations. From the perspective of transport engineering, there are several ways to approach this issue, from the design of infrastructure and vehicles to passenger traffic management. The tools normally available to traffic engineers are analytical models, microscopic traffic simulation, and, ultimately, real-scale laboratory experiments. In any case, the data that are required are number of passengers that get on and off from the vehicles, as well as the number of passengers waiting on platforms. Traditionally, such data has been collected manually by field counts or through videos that are then processed by hand. On the other hand, public transport networks, specially metropolitan railways, have an extensive monitoring infrastructure based on standard video cameras. Traditionally, these are observed manually or with very basic signal processing support, so there is significant scope for improving data capture and for automating the analysis of site usage, safety, and surveillance. This article shows a way of collecting and analyzing the data needed to feed both traffic models and analyze laboratory experimentation, exploiting recent intelligent sensing approaches. The paper presents a new public video dataset gathered using real-scale laboratory recordings. Part of this dataset has been annotated by hand, marking up head locations to provide a ground-truth on which to train and evaluate deep learning detection and tracking algorithms. Tracking outputs are then used to count people getting on and off, achieving a mean accuracy of 92% with less than 0.15% standard deviation on 322 mostly unseen dataset video sequences.Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander. Rodrigo Fernandez and Sergio A. Velastin gratefully acknowledge the Chilean National Science and Technology Council (Conicyt) for its funding under CONICYT-Fondecyt Regular Grant Nos. 1120219, 1080381 and 1140209 (“OBSERVE”)

Cellulose Nanofiber-Based Aerogels from Wheat Straw: Influence of Surface Load and Lignin Content on Their Properties and Dye Removal Capacity

Water pollution is one of the most serious problems worldwide. Nanocellulose-based aerogels usually show excellent adsorption capacities due to their high aspect ratio, specific surface area and surface charge, making them ideal for water purification. In this work, (ligno)cellulose nanofibers (LCNFs/CNFs) from wheat straw residues were obtained using two types of pre-treatments: mechanical (Mec) and TEMPO-mediated oxidization (TO), to obtain different consistency (0.2, 0.4, 0.6 and 0.8) bioaerogels, and their adsorption capacities as dye removers were further studied. The materials were characterized in terms of density, porosity and mechanical properties. An inversely proportional relationship was observed between the consistencies of the aerogels and their achieved densities. Despite the increase in density, all samples showed porosities above 99%. In terms of mechanical properties, the best results were obtained for the 0.8% consistency LCNF and CNF-Mec aerogels, reaching 67.87 kPa and 64.6 kPa for tensile strength and Young’s modulus, respectively. In contrast, the adsorption capacity of the aerogels was better for TEMPO-oxidized aerogels, reaching removal rates of almost 100% for the CNF-TO5 samples. Furthermore, the residual lignin content in LCNF-Mec aerogels showed a great improvement in the removal capacity, reaching rates higher than 80%, further improving the cost efficiency of the samples due to the reduction in chemical treatments

Effect of a DC Electric field on the melting temperature, nucleation and ice growth rate of the TIP4P/ICE water model

Understanding the effect of electric fields on the thermal stability and phase transitions of water could have potential applications in the food industry, cryopreservation, and environmental science. In this work, we investigate the effect of a static electric field on the melting temperature (Tm), ice nucleation and ice growth rate of two phases of ice, hexagonal ice (Ih) and ferroelectric cubic ice (Icf), for the TIP4P/ICE water model. By means of direct coexistence simulations, we establish that Tm of Ice Ih is shifted toward lower values, whereas Tm of Ice Icf grows, becoming the most stable ice phase for sufficiently largevalues of the applied electric field. We also investigate ice nucleation for both ice phases under an external electric field and find that, for a given supercooling with respect to Tm, while the field slows down the nucleation rate of ice Ih significantly, it barely affects that of ice Icf, due to the enhanced ability of water molecules to orient favorably along the direction of the field in the latter phase. In terms of absolute temperature, overall ice formation is promoted by the electric field because it increases the melting point of ice Icf. Finally, we show how the electric field slows down the crystal growth of Ice Ih and increases that of Ice Icf by a factor of about two

Modelamiento de distribución de productos cárnicos como un TSP (Traveling Salesman Problem) con teoría de grafos

This paper has as its main objective of use the TSP (Traveling Salesman Problem) with graph theory in order to design routing strategies in a distribution network, looking for an efficient way to be made in regard to distance and time used for attending customer’s requirements. Additionally, the paper develops a case of application of the methodology in a Meat Company located in the city of Medellín. As a result, we find that the use of TSP with graph produces a route with the minimum distance in the distribution network; it was demonstrated in the case of application.El presente artículo tiene como objetivo utilizar el TSP (Traveling Salesman Problem) junto con teoría de grafos para diseñar estrategias de ruteo en una red distribución, buscando que esta se realice de manera eficiente respecto a la distancia y tiempos utilizados en la atención de los pedidos de los clientes. Adicionalmente, se desarrolla un caso de aplicación de la metodología en una empresa de cárnicos ubicada en la ciudad de Medellín. Como resultado, se obtiene que la utilización de TSP con grafos, permite obtener una ruta con la mínima distancia en la red de distribución, lo que se demuestra en el caso de aplicación