Joint Object Detection and Depth Estimation in Multiplexed Image
| Autor: | Pongsak Lasang, Changxin Zhou, Yazhou Liu, Quansen Sun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
multiplexed image
General Computer Science Object detection Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences 01 natural sciences Multiplexing Convolution Computational photography depth estimation 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision 0105 earth and related environmental sciences business.industry Detector 3D reconstruction General Engineering Object (computer science) 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 123107-123115 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2936126 |
| Popis: | This paper presents an object detection method that can simultaneously estimate the positions and depth of the objects from multiplexed images. Multiplexed image 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 computational 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 proposed 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 the simulated multiplexed image using the stereo images from KITTI, and very encouraging results have been obtained. |
| Databáze: | OpenAIRE |
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