Vision based obstacle detection and avoidance using low level image features

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2006

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Senlet, Turgay

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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

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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.