• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.481-486
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• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using differential haar-like feature, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: horizontal haar-like feature and vertical haar-like feature. For the next, we calculate the proposed feature vector using differential haar-like method. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using the differential area of horizontal and vertical haar-like. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method for the pedestrian and non-pedestrian.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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• pp.336-344
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• 2011

This article describes a new recognition algorithm using correlation coefficient for intelligent wheelchair to avoid collision for elderly or disabled people. The correlation coefficient can be used to represent the relationship of two different areas. The algorithm has three steps: Firstly, we extract an edge vector using the Histogram of Oriented Gradients(HOG) which includes gradient information and unique magnitude for each cell. From this result, the correlation coefficients are calculated between one cell and others. Secondly, correlation coefficients are used as the weighting factors for normalizing the HOG cell. And finally, these features are used to classify or detect variable and complicated shapes of two wheelers using Adaboost algorithm. In this paper, we propose a new feature vectors which is calculated by weighted cell unit to classify with multiple view-based shapes: frontal, rear and side views($60^{\circ}$, $90^{\circ}$ and mixed angle). Our experimental results show that two wheeler detection system based on a proposed approach leads to a higher detection accuracy than the method using traditional features in a similar detection time.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.10
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• pp.936-940
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• 2013

As the ageing population grows around the world, the demand for electric wheelchairs, an important mobility assistance device for the disabled and elderly, is gradually increasing. Therefore, a number of studies related to intelligent wheelchairs are actively underway to improve safety and comfort for wheelchair users. However, previous collision avoidance studies for intelligent wheelchairs have concentrated on collision avoidance methods with the shortest distance and by only changing either velocity or heading angle, rather than considering the forces exerted on the user. If a collision avoidance algorithm that does not consider these forces is applied to an intelligent wheelchair, there is a possibility of an accident due to falling as wheelchair users are generally disabled and elderly people. In this paper, we propose a collision avoidance algorithm which minimizes the forces exerted on a wheelchair user by minimizing the variation of the wheelchair's velocity and heading angle when the sizes, positions, velocities, and heading angles of a wheelchair and a moving obstacle are known.