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2019 | OriginalPaper | Buchkapitel

Joint Object Detection and Depth Estimation in Multiplexed Image

verfasst von : Changxin Zhou, Yazhou Liu

Erschienen in: Intelligence Science and Big Data Engineering. Visual Data Engineering

Verlag: Springer International Publishing

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Abstract

This paper presents an object detection method that can simultaneously estimate the positions and depth of the objects from multiplexed images. Multiplexed image [28] is produced by a new type of imaging device that collects the light from different fields of view using a single image sensor, which is originally designed for stereo, 3D reconstruction and broad view generation using computation imaging. Intuitively, multiplexed image is a blended result of the images of multiple views and both of the appearance and disparities of objects are encoded in a single image implicitly, which provides the possibility for reliable object detection and depth/disparity estimation. Motivated by the recent success of CNN based detector, a multi-anchor detector method is proposed, which detects all the views of the same object as a clique and uses the disparity of different views to estimate the depth of the object. The method is interesting in the following aspects: firstly, both locations and depth of the objects can be simultaneously estimated from a single multiplexed image; secondly, there is almost no computation load increase comparing with the popular object detectors; thirdly, even in the blended multiplexed images, the detection and depth estimation results are very competitive. There is no public multiplexed image dataset yet, therefore the evaluation is based on simulated multiplexed image using the stereo images from KITTI, and very encouraging results have been obtained.

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Metadaten
Titel
Joint Object Detection and Depth Estimation in Multiplexed Image
verfasst von
Changxin Zhou
Yazhou Liu
Copyright-Jahr
2019
Buch
Intelligence Science and Big Data Engineering. Visual Data Engineering

Print ISBN: 978-3-030-36188-4

Electronic ISBN: 978-3-030-36189-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-36189-1_26