Research method detection human face in video streams

То же на с. 58-6

REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed

Conditional Regressive Random Forest Stereo-based Hand Depth Recovery

This paper introduces Conditional Regressive Random Forest (CRRF), a novel method that combines a closed-form Conditional Random Field (CRF), using learned weights, and a Regressive Random Forest (RRF) that employs adaptively selected expert trees. CRRF is used to estimate a depth image of hand given stereo RGB inputs. CRRF uses a novel superpixel-based regression framework that takes advantage of the smoothness of the hand’s depth surface. A RRF unary term adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. CRRF also includes a pair-wise term that encourages smoothness between similar adjacent superpixels. Experimental results show that CRRF can produce high quality depth maps, even using an inexpensive RGB stereo camera and produces state-of-the-art results for hand depth estimation

Hand Pose Estimation Using Deep Stereovision and Markov-chain Monte Carlo

Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This paper seeks to address this gap by presenting a datadriven method to estimate a hand pose from a stereoscopic camera input, by introducing a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, our method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa. Experimental results using an inexpensive stereo camera show that the proposed system more accurately measures pose better than competing methods

Harjateräksen ominaisuuksien mittaaminen konenäön avulla

Harjateräksen valmistukseen on tarkat standardit, joiden noudattamista viranomaistaho valvoo mittaamalla tuotannosta otettavia näytepaloja aika ajoin. Harjateräsvalmistajat mittaavat tuotteitaan varmistuakseen vaatimusten täyttymisestä. Pintos Oy:llä mittaukset on tehty tähän asti käsin muun muassa työntömitalla. Tähän kaivattiin automatisointia lähinnä mittaustoistettavuuden parantamiseksi. Projekti aloitettiin tutkimalla erilaisia harjaterästangon mittaustapoja. Aluksi tutkittiin erilaisia mekaanisia tekniikoita, mutta konenäköjärjestelmän edut nousivat muiden tekniikoiden edelle. Suunniteltaessa konenäköjärjestelmää todettiin, että on hyödyllistä tehdä harjaterästangosta 3D-malli, josta pystytään laskemaan tarvittavat ominaisuudet. 3D-mallin tekemiseen päädyttiin, koska 2D-kuva ei sisällä tarpeeksi tietoa vaadittaviin mittauksiin. Myös kuvaustekniikan valinta oli haasteellista harjaterästangon muodon ja materiaalin vuoksi. Lopulta taustavaloa vasten eri kulmista otettujen kuvien yhdistäminen ja niistä 3D-mallin muodostaminen osoittautui hyväksi ratkaisuksi. Idean toteuttamiskelpoisuus todettiin erilaisten testausversioiden avulla. Osa järjestelmän komponenteista, kuten kamera ja optiikka, ostettiin valmiina, mutta suurin osa suunniteltiin ja toteutettiin varta vasten tätä pilottijärjestelmää varten. Lopullinen harjateräksen ominaisuuksien mittaamiseen tehty pilottijärjestelmä koostuu telineestä, siihen integroidusta kamerasta, optiikasta, taustavalosta, askelmoottorista ja ohjauselektroniikasta sekä niitä ohjaavasta ohjelmistosta. Järjestelmä mittaa harjaterästangon harjakorkeuden, harjavälin, harjakulman, harjan nousukulman, harjan poikkipinta-alan sekä harjarivien välisen etäisyyden.Reinforcing bar (re-bar) manufacturing is strictly standardized. Certified third party checks that the standards are followed by measuring the re-bar samples taken from the production line. Also the manufacturers measure the re-bars time to time to make sure that their re-bars fulfil the requirements set by the standard. Pintos Oy used mostly caliper in measuring re-bar characteristics. This measuring process needed to be automated. The project started by studying different kind of possible measuring techniques for re-bars. The testing started with different kind of mechanical measuring methods, but shortly machine vision was discovered to be the most potential method. In this designing process the 3D model was found to be a useful way to measure the needed characteristics because 2D image did not give enough information. It was also very challenging to choose the right imaging technique because of the shape and material of the re-bar. Finally, creating 3D model of a re-bar by combining images taken from different angles of the re-bar was found to be a very successful solution. The viability of the idea was proven with different kind of test versions. Some of the components like camera and optics used in this system were bought, but most of the parts were designed and purpose-built. The final pilot system made for measuring the re-bar characteristics consists of a platform, a camera, an optics, a back light, a stepper motor and a controlling electronics but also of a program which controls the whole system. This system measures the rib height, the rib angle, the distance between the ribs, rib’s longitudinal cross-section area, the rib flank inclination and a distance between rib rows

Estudi comparatiu de la publicació científica de la UPC i l’ETSETB vs. altres universitats (2006-2016)

L'informe es centra en la publicació científica especialitzada en l'àmbit temàtic propi de l'ETSETB: l'enginyeria de telecomunicacions i l'electrònica. Es comparen indicadors bibliomètrics de la UPC i l'ETSETB amb els d'altres universitats nacionals, europees i internacionals amb activitat de recerca notable en l'àrea de les telecomunicacions i l'electrònica.Postprint (published version