Kinect-Based Algorithms for Motion Analysis
نویسندگان
چکیده
Motion detection is an essential block of multiple systems. There are usually some constrains related to the motion of the camera, or the type of scene. This work proposes a framework to detect and classify multiple moving objects in an 3D space using the Microsoft Kinect camera without any motion constraint. To solve the detection problem, a 3D alignment step is carried out using the Iterative Closest Point(ICP) algorithm followed by multiple 2D frame-difference techniques, ending up with the construction of 3D clusters containing the moving objects. Two types of entities were considered on this work people and balls. To classify people, a classification algorithm relying on Histograms of Oriented Gradients(HOG) is built. A Random Sample Consensus(RANSAC) model is used to find balls. The proposed solution was tested in realistic indoor scenes.
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