Browsing by Subject "Collision avoidance"

Now showing 1 - 4 of 4

Open Access
Augmentation of virtual agents in real crowd videos
(Springer, 2019) Doğan, Yalım; Demirci, Serkan; Güdükbay, Uğur; Dibeklioğlu, Hamdi
Augmenting virtual agents in real crowd videos is an important task for different applications from simulations of social environments to modeling abnormalities in crowd behavior. We propose a framework for this task, namely for augmenting virtual agents in real crowd videos. We utilize pedestrian detection and tracking algorithms to automatically locate the pedestrians in video frames and project them into our simulated environment, where the navigable area of the simulated environment is available as a navigation mesh. We represent the real pedestrians in the video as simple three-dimensional (3D) models in our simulation environment. 3D models representing real agents and the augmented virtual agents are simulated using local path planning coupled with a collision avoidance algorithm. The virtual agents augmented into the real video move plausibly without colliding with static and dynamic obstacles, including other virtual agents and the real pedestrians.
Open Access
Comparative analysis of different approaches to target differentiation and localization with sonar
(Elsevier, 2003) Barshan, B.; Ayrulu, B.
This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-2ight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained usingpre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Open Access
A comparison of two methods for fusing information from a linear array of sonar sensors for obstacle localization
(IEEE, 1995) Arıkan, Orhan; Barshan, Billur
The performance of a commonly employed linear array of sonar sensors is assessed for point-obstacle localization intended for robotics applications. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the obstacle: pairwise estimate method and the maximum likelihood estimator. The variances of the methods are compared to the Cramer-Rao Lower Bound, and their biases are investigated. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation. The results are useful for target localization in mobile robotics.
Open Access
Surface profile determination from multiple sonar data using morphological processing
(Sage Publications Ltd., 1999-08) Başkent, D.; Barshan, B.
This paper presents a novel method for surface profile determination using multiple sensors. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The algorithm is verified both by simulating and experiments in the laboratory by processing real sonar data obtained from a mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is, however, more complex and expensive.