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Theses

Event-based detection and tracking

Abstract : The main goal of this thesis is the development of event-based algorithms for visual detection and tracking. This algorithms are specifically designed to work on the output of neuromorphic event-based cameras. This type of cameras are a new type of bioinspired sensors, whose principle of operation is based on the functioning of the retina: every pixel is independent and generates events asynchronously when a sufficient amount of change is detected in the luminance at the corresponding position on the focal plane. This new way of encoding visual information calls for new processing methods. First, a part-based shape tracking is presented, which represents an object as a set of simple shapes linked by springs. The resulting virtual mechanical system is simulated with every incoming event. Next, a line and segment detection algorithm is introduced, which can be employed as an event-based low level feature. Two event-based methods for 3D pose estimation are then presented. The first of these 3D algorithms is based on the assumption that the current estimation is close to the true pose of the object, and it consequently requires a manual initialization step. The second of the 3D methods is designed to overcome this limitation. All the presented methods update the estimated position (2D or 3D) of the tracked object with every incoming event. This results in a series of trackers capable of estimating the position of the tracked object with microsecond resolution. This thesis shows that event-based vision allows to reformulate a broad set of computer vision problems, often resulting in simpler but accurate algorithms.
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Submitted on : Monday, September 17, 2018 - 4:46:07 PM
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  • HAL Id : tel-01875724, version 1

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David Reverter Valeiras. Event-based detection and tracking. Robotics [cs.RO]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066566⟩. ⟨tel-01875724⟩

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