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Intelligent embedded camera for robust object tracking on mobile platform

Abstract : Initially, the aim of this study is to analyze, compare and retain the most relevant tracking methods likely to respect the constraints of embedded systems, such as Micro Aerial Vehicles (MAVs), Unmanned Aerial Vehicles (UAVs), intelligent glasses, textit{etc.} in order to find a new robust embedded tracking system. A typical VINS consists of a monocular camera that provides visual data (frames), and a low-cost Inertial Measurement Unit (IMU), a Micro-Electro-Mechanical System (MEMS) that measures inertial data. This combination is very successful in the application field of system navigation thanks to the advantages that these sensors provide, mainly in terms of accuracy, cost and rapid reactivity. Over the last decade, various sufficiently accurate tracking algorithms and VINS have been developed, however, they require greater computational resources. In contrast, embedded systems are characterized by their high integration constraints and limited resources. Thus, a solution to embedded architecture must be based on efficient algorithms that provide less computational load.As part of this work, various tracking algorithms identified in the literature are discussed, focusing on their accuracy, robustness, and computational complexity. In parallel to this algorithmic survey, numerous recent embedded computation architectures, especially those dedicated to visual and/or visual-inertial tracking, are also presented. In this work, we propose a robust visual-inertial tracking method, called: "Context Adaptive Visual Inertial SLAM". This approach is adaptive to different system navigation context and well fitted for embedded systems such as MAVs. It focuses on the analysis of the impact of navigation condition on the tracking process in an embedded system. It also provides an execution control module able to switch between the most relevant tracking approaches by monitoring its internal state and external inputs variables. The main objective is to ensure tracking continuity and robustness despite difficult conditions
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Submitted on : Thursday, April 15, 2021 - 10:09:18 AM
Last modification on : Saturday, January 22, 2022 - 3:32:22 AM
Long-term archiving on: : Friday, July 16, 2021 - 6:20:04 PM


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  • HAL Id : tel-03198776, version 3



Imane Salhi. Intelligent embedded camera for robust object tracking on mobile platform. Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Est, 2021. English. ⟨NNT : 2021PESC2001⟩. ⟨tel-03198776v3⟩



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