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In this paper, a robust, real-time object tracking approach capable of dealing with multiple symmetric and non-symmetric objects in a real-time requirement setting is proposed. The approach relies only on depth data to track multiple objects in a dynamic environment and uses random-forest based learning to deal with problems like object occlusion, motion-blur due to camera motion and clutter. We show...
Human action recognition plays a vital role in the field of human-robot interaction and is widely researched for its potential applications. In this paper we propose a human action recognition framework for human robot interaction in industrial applications. First, a set of key descriptors are learned from a collection of weak spatio-temporal skeletal joint descriptors using random forests, which...
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