Real-time visual tracking of 3D objects with dynamic handling of occlusion

By P. Wunsch, G. Hirzinger. 1997

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Domain randomization for transferring deep neural networks from simulation to the real world (2017, background): “Object detection and pose estimation for robotics is a well-studied problem in the literature (see, e.g., [4], [5], [6], [10], [44], [50], [54]).”
Graspit! A versatile simulator for robotic grasping (2004, background): “, 2000] [Wunsch and Hirzinger, 1997] while feature based techniques use distinct image features: corners, lines, regions [Hager and Toyama, 1998].”
A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “The Hough transform was utilized to achieve a similar idea in [208].”
Survey on Visual Servoing for Manipulation (2002, methodology): “(Wunsch & Hirzinger 1997) and (Wunsch et al. 1997) present a method for model based pose estimation of an object based on a neural net approach.”
Object tracking using SIFT features and mean shift (2009, methodology): “The Hough transform was utilized to achieve a similar idea in [24].”
Object recognition and pose estimation using color cooccurrence histograms and geometric modeling (2005, background): “[33].”