This paper studies the notion of hierarchical (chained) structure of stochastic tracking of marked feature points while a person is moving in the field of view of a RGB and depth sensor. The objective is to explore how the information between the two sensing modalities (namely, RGB sensing and depth sensing) can be cascaded in order to distribute and share the implicit knowledge associated with the tracking environment. In the first layer, the prior estimate of the state of the object is distributed based on the novel expected motion constraints approach associated with the movements. For the second layer, the segmented output resulting from the RGB image is used for tracking marked feature points of interest in the depth image of the person. Here we proposed two approaches for associating a measure (weight) for the distribution of the estimates (particles) of the tracking feature points using depth data. The first measure is based on the notion of spin-image and the second is based on the geodesic distance. The paper presents the overall implementation of the proposed method combined with some case study results.
Liu, X., & Payandeh, S. (2018). A Study of Chained Stochastic Tracking in RGB and Depth Sensing. Journal of Control Science and Engineering, 2018, 1–10. DOI: 10.1155/2018/2605735.
Journal of Control Science and Engineering
A Study of Chained Stochastic Tracking in RGB and Depth Sensing
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