Fengda Zhao

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In the traditional particle filter tracking system, the weight of each particle is determined only by Bhattacharyya coefficient of two corresponding color histogram, which may easily lead to error tracking when the object and background have similar color distribution. In this paper, a novel particle filter algorithm is proposed in which the weight of(More)
Learning human daily behavior habit patterns from sensor data is very important for high-level activity inference of service robot. This paper proposes a model that represents person's daily behavior habit pattern. Firstly, a coordinate frame is defined on a map built by mobile service robot, and two key variables are calculated using consecutive data(More)
To solve the problem of accuracy and robustness seriously affected by the external environment when loop-closing using vision for mobile robot, a method based on RGB-D image for loop-closing detection is proposed. The front area contour which will be used for contour matching is extracted from the depth information. Then Image matching between similar(More)
Detecting the person's unwonted behavior state timely and correctly in home environment for the service robot is a fundamental problem. This paper proposes an approach to this problem using the position and pose information as the features and the computing result of the CRF model as the judging gist. Firstly, a pose template based on position label is(More)
To improve the localization capability of the mobile robot in a crowded and unorderly indoor environment, an approach for extracting LSRII (Local Salient Region Integral Invariant) features is proposed. The approach extracts integral invariant features in salient regions in an image. The global localization is achieved by applying LSRII features in particle(More)
This paper proposes an approach by using the position information in home environment to learn human daily behavior habit from sensor data. Firstly, we build a layered framework which is suitable for service robot and can be applied in home environment to monitor the behavior states of elderly human. Then, we apply the improved basic sequential algorithmic(More)
For the purpose of learning human daily behavior habit from sensor data to detect the unwonted behavior, we propose an approach by using the position information in home environment. Firstly, we build a layered framework which is suitable for service robot and can be applied in home environment to monitor the behavior states of elderly human. Then, we apply(More)
To improve the localization capability of the mobile robot in a dynamic environment, we propose a vision-based Monte-Carlo localization approach. We apply the image retrieval technique to compute the similarity between query images and the images stored in an image database. In order to reduce the effect of images matching cased by illumination change,(More)
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