Object Detection in the Context of Mobile Augmented Reality

• URL: http://arxiv.org/abs/2008.06655v1
• Date: Sat, 15 Aug 2020 05:15:00 GMT
• Title: Object Detection in the Context of Mobile Augmented Reality
• Authors: Xiang Li and Yuan Tian and Fuyao Zhang and Shuxue Quan and Yi Xu
• Abstract summary: We propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices. Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map. The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.
• Score: 16.49070406578342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past few years, numerous Deep Neural Network (DNN) models and frameworks have been developed to tackle the problem of real-time object detection from RGB images. Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment. On the other hand, mobile Augmented Reality (AR) frameworks can continuously track a camera's pose within the scene and can estimate the correct scale of the environment by using Visual-Inertial Odometry (VIO). In this paper, we propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices. Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map. Each component takes advantage of the different characteristics of the VIO-based AR framework. We implemented the AR-enhanced features using ARCore and the SSD Mobilenet model on Android phones. To validate our approach, we manually labeled objects in image sequences taken from 12 room-scale AR sessions. The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.

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