Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions

Inimeste tuvastamine ning kauguse hindamine kasutades kaamerat ning YOLOv3 tehisnärvivõrku

Inimestega vähemalt samal tasemel keskkonnast aru saamine masinate poolt oleks kasulik paljudes domeenides. Mitmed erinevad sensored aitavad selle ülesande juures, enim on kasutatud kaameraid. Objektide tuvastamine on tähtis osa keskkonnast aru saamisel. Selle täpsus on viimasel ajal palju paranenud tänu arenenud masinõppe meetoditele nimega konvolutsioonilised närvivõrgud (CNN), mida treenitakse kasutades märgendatud kaamerapilte. Monokulaarkaamerapilt sisaldab 2D infot, kuid ei sisalda sügavusinfot. Teisalt, sügavusinfo on tähtis näiteks isesõitvate autode domeenis. Inimeste ohutus tuleb tagada näiteks töötades autonoomsete masinate läheduses või kui jalakäija ületab teed autonoomse sõiduki eest. Antud töös uuritakse võimalust, kuidas tuvastada inimesi ning hinnata nende kaugusi samaaegselt, kasutades RGB kaamerat, eesmärgiga kasutada seda autonoomseks sõitmiseks maastikul. Selleks täiustatakse hetkel parimat objektide tuvastamise konvolutsioonilist närvivõrku YOLOv3 (ingl k. You Only Look Once). Selle töö väliselt on simulatsioonitarkvaradega AirSim ning Unreal Engine loodud lumine metsamaastik koos inimestega erinevates kehapoosides. YOLOv3 närvivõrgu treenimiseks võeti simulatsioonist välja vajalikud andmed, kasutades skripte. Lisaks muudeti närvivõrku, et lisaks inimese asukohta tuvastavale piirikastile väljastataks ka inimese kauguse ennustus. Antud töö tulemuseks on mudel, mille ruutkesmine viga RMSE (ingl k. Root Mean Square Error) on 2.99m objektidele kuni 50m kaugusel, säilitades samaaegselt originaalse närvivõrgu inimeste tuvastamise täpsuse. Võrreldavate meetodite RMSE veaks leiti 4.26m (teist andmestikku kasutades) ja 4.79m (selles töös kasutatud andmestikul), mis vastavalt kasutavad kahte eraldiseisvat närvivõrku ning LASSO meetodit. See näitab suurt parenemist võrreldes teiste meetoditega. Edasisteks eesmärkideks on meetodi treenimine ning testimine päris maailmast kogutud andmetega, et näha, kas see üldistub ka sellistele keskkondadele.Making machines perceive environment better or at least as well as humans would be beneficial in lots of domains. Different sensors aid in this, most widely used of which is monocular camera. Object detection is a major part of environment perception and its accuracy has greatly improved in the last few years thanks to advanced machine learning methods called convolutional neural networks (CNN) that are trained on many labelled images. Monocular camera image contains two dimensional information, but contains no depth information of the scene. On the other hand, depth information of objects is important in a lot of areas related to autonomous driving, e.g. working next to an automated machine, pedestrian crossing a road in front of an autonomous vehicle, etc. This thesis presents an approach to detect humans and to predict their distance from RGB camera for off-road autonomous driving. This is done by improving YOLO (You Only Look Once) v3[1], a state-of-the-art object detection CNN. Outside of this thesis, an off-road scene depicting a snowy forest with humans in different body poses was simulated using AirSim and Unreal Engine. Data for training YOLOv3 neural network was extracted from there using custom scripts. Also, network was modified to not only predict humans and their bounding boxes, but also their distance from camera. RMSE of 2.99m for objects with distances up to 50m was achieved, while maintaining similar detection accuracy to the original network. Comparable methods using two neural networks and a LASSO model gave 4.26m (in an alternative dataset) and 4.79m (with dataset used is this work) RMSE respectively, showing a huge improvement over the baselines. Future work includes experiments with real-world data to see if the proposed approach generalizes to other environments

A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision

Advanced driving assistance systems (ADAS) form a complex multidisciplinary research field, aimed at improving traffic efficiency and safety. A realistic analysis of the requirements and of the possibilities of the traffic environment leads to the establishment of several goals for traffic assistance, to be implemented in the near future (ADASE, INVENT