Vision based obstacle detection and avoidance using low level image features

Download

index.pdf

Date

2006

Author

Senlet, Turgay

Metadata

Show full item record

Item Usage Stats

216
views

123
downloads

This study proposes a new method for obstacle detection and avoidance using low-level MPEG-7 visual descriptors. The method includes training a neural network with a subset of MPEG-7 visual descriptors extracted from outdoor scenes. The trained neural network is then used to estimate the obstacle presence in real outdoor videos and to perform obstacle avoidance. In our proposed method, obstacle avoidance solely depends on the estimated obstacle presence data. In this study, backpropagation algorithm on multi-layer perceptron neural network is utilized as a feature learning method. MPEG-7 visual descriptors are used to describe basic features of the given scene image and by further processing these features, input data for the neural network is obtained. The learning/training phase is carried out on specially constructed synthetic video sequence with known obstacles. Validation and tests of the algorithms are performed on actual outdoor videos. Tests on indoor videos are also performed to evaluate the performance of the proposed algorithms in indoor scenes. Throughout the study, OdBot 2 robot platform, which has been developed by the author, is used as reference platform. For final testing of the obstacle detection and avoidance algorithms, simulation environment is used. From the simulation results and tests performed on video sequences, it can be concluded that the proposed obstacle detection and avoidance methods are robust against visual changes in the environment that are common to most of the outdoor videos. Findings concerning the used methods are presented and discussed as an outcome of this study.

Subject Keywords

Computer engineering.

URI

http://etd.lib.metu.edu.tr/upload/12607229/index.pdf
https://hdl.handle.net/11511/15900

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Nonlinear dynamic modeling of gear-shaft-disk-bearing systems using finite elements and describing functions Maliha, R; Dogruer, CU; Özgüven, Hasan Nevzat (ASME International, 2004-05-01) This study presents a new nonlinear dynamic model for a gear-shaft-disk-bearing system. A nonlinear dynamic model of a spur gear pair is coupled with linear finite element models of shafts carrying them, and with discrete models of bearings and disks. The nonlinear elasticity term resulting from backlash is expressed by a describing function, and a method developed in previous studies to determine multi harmonic responses of nonlinear multi-degree-of-freedom systems is employed for the solution. The excitat...
Routing optimization methods for communication networks Demircan, Ahmet Emrah; Leblebicioğlu, Mehmet Kemal; Department of Electrical and Electronics Engineering (2005) This study discusses the routing optimization techniques and algorithms for communication networks. Preventing data loss on overloaded communication links and utilizing link bandwidths efficiently are the main problems of traffic engineering. Load balancing and routing problems are solved using both by heuristics such as genetic algorithms, and simulation techniques. These algorithms work on destination-based or flow-based routing techniques and mainly change the link weight system or try to select the best...
Multi-camera video surveillance : detection, occlusion handling, tracking and event recognition Akman, Oytun; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2007) In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tr...
Prediction of slip in cable-drum systems using structured neural networks KILIÇ, Ergin; Dölen, Melik (SAGE Publications, 2014-02-01) This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also hig...
Video Shot Boundary Detection by Graph-theoretic Dominant Sets Approach Asan, Emrah; Alatan, Abdullah Aydın (2009-09-16) We present a video shot boundary detection algorithm based on the novel graph theoretic concept, namely dominant sets. Dominant sets are defined as a set of the nodes in a graph, mostly similar to each other and dissimilar to the others. In order to achieve this goal, candidate shot boundaries are determined by using simply pixelwise differences between consequent frames. For each candidate position, a testing sequence is constructed by considering 4 frames before the candidate position and 2 frames after t...

Citation Formats

T. Senlet, “Vision based obstacle detection and avoidance using low level image features,” M.S. - Master of Science, Middle East Technical University, 2006.