Depth Estimation of Semi-submerged Objects Using a Light-Field Camera

Fan, Juehui; Yang, Yee‐Hong

doi:10.1109/crv.2017.44

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Zhao et al [25] believed that light-field camera could be applied to depth extraction of low-texture region. Fan and Yang [26] estimated the depth of real-world scenes containing an object semi-submerged in water using a light-field camera. The depth estimation of the light-field imaging had a certain develop [27], [28] and it made many applications possible, e.g., 3D scene reconstruction [29], saliency detection [30], and image super-resolution reconstruction [31].…”

Section: Related Work a Light Field Imagingmentioning

confidence: 99%

Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

et al. 2020

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Great progress has been made in the field of pedestrian detection, but the following two problems have not yet been well addressed. One problem is the missed detection of small scale pedestrain as false negative failure, and the other one is confusion with anthropomorphic negative samples like vertical structures as false positive failure. In this paper, to tackle the above two problems, we use the light-field camera to capture pedestrian images for the following reasons: (i) the light-field camera can obtain multidepth refocused images in a single exposure using one sensor, (ii) compared with 2D images, these refocused images can provide different key representations for different parts of the image. We further establish a light-field pedestrian dataset with 1766 images for pedestrian detection. A multi-focus detection network proposed in this work consists of multiple-branch detection models and takes multiple refocused images as inputs. In order to select the appropriate candidate proposal bounding box as final detection results, we design a cumulative probability selection (CPS) layer to combine each refocused image branch and accumulate the probability of each candidate neighboring proposal. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods on our light-field pedestrian dataset. INDEX TERMS CNN, light-field imaging, pedestrian detection.

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Section: Related Work a Light Field Imagingmentioning

confidence: 99%

Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

et al. 2020

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Light-field imaging for distinguishing fake pedestrians using convolutional neural networks

Zhao

Shi

et al. 2021

International Journal of Advanced Robotic Systems

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Pedestrian detection plays an important role in automatic driving system and intelligent robots, and has made great progress in recent years. Identifying the pedestrians from confused planar objects is a challenging problem in the field of pedestrian recognition. In this article, we focus on the 2D fake pedestrian identification based on light-field (LF) imaging and convolutional neural network (CNN). First, we expand the previous dataset to 1500 samples, which is a mid-size dataset for LF images in all public LF datasets. Second, a joint CNN classification framework is proposed, which uses both RGB image and depth image (extracted from the LF image) as input. This framework can fully mine 2D feature information and depth feature information from corresponding images. The experimental results show that the proposed method is efficient to identify the fake pedestrian in a 2D plane and achieves a recognition accuracy of 97.0%. This work is expected to be used in recognition of 2D fake pedestrian and may help researchers solve other computer vision problems.

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Depth Estimation of Semi-submerged Objects Using a Light-Field Camera

Cited by 2 publications

References 14 publications

Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

Light-field imaging for distinguishing fake pedestrians using convolutional neural networks

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