Moving Shadow Detection Algorithm Using Multiple Features

Xie, Wei; Zhao, Jiyong; Hu, Yanming

doi:10.1109/csa.2015.60

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Among the above-mentioned methods, multi-feature shadow detection and elimination method based on combination of color feature and texture feature is widely used. Xie et al [18] described a histogram of regional gradient direction to describe texture feature and then combined the color feature to detect shadows. Dai et al [19] combined HSV color feature and LBP feature to eliminate shadows.…”

Section: Related Workmentioning

confidence: 99%

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Zhang

Li³

et al. 2020

IEEE Access

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Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.

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Section: Related Workmentioning

confidence: 99%

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Zhang

Li³

et al. 2020

IEEE Access

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“…The method presented in [21] utilized HSV color features and edge features along with irradiance information for modeling shadows. The methods presented in [22,23] used color, texture, and gradient cues simultaneously for detecting shadow pixels. In [24,25], shadow detected through fusing color and texture information using multiple descriptors for these cues.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 summarizes the comparison of advantages and disadvantages of the previous methods and the proposed method for shadow detection. [21] Gradient and color [22,23] Color and texture fusion [24,25] Optical reflection invariance [26] Illumination invariant cues [27] Learning based techniques GMM [28] -…”

Section: Related Workmentioning

confidence: 99%

Mask R-CNN for Moving Shadow Detection and Segmentation

Bakr

Hamad

Amin

2020

IJCI. International Journal of Computers and Information

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One of the primary tasks of completing and developing many computer vision applications is to identify and remove shadow regions. Most existing moving shadow detection methods depend on extracting hand-crafted features of object and shadow regions manually (for example the chromaticity, physical, or geometric properties). Shadow detection using handcrafted features is a challenging task due to different environmental conditions of the shadow such as camouflage and illumination irregularity problems that make these features inefficient to handle such problems. The proposed method uses Convolution Neural Networks (CNN) to automatically learn different distinctive features to model shadow under different environmental conditions. In this paper, the Mask Region Convolution Neural Network (Mask R-CNN) framework is evaluated and tested to automatically perform semantic segmentation in order to detect and classify shadow pixels from the entire video frame. To adapt Mask R-CNN for segmenting and detecting shadow regions, the most significant features are extracted from video frames in a supervised way using deep Residual Network (ResNet-101) architecture. Then, the Region proposal network (RPN) predicts regions of interest (ROI) and their classes that contain foreground objects. Finally, Fully Convolutional Network (FCN) generates a binary segmentation mask for each detected class in ROI. The proposed framework evaluated on common shadow detection datasets that have different environmental issues. Experimental results achieved high performance rates compared to several state-of-the-art methods in terms of average detection rate (96.81%), average discrimination rate (99.42%), and overall accuracy (98.09 %).

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Near-infrared shadow detection based on HDR image

Zhang

Hua³

2022

Multimed Tools Appl

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Moving Shadow Detection Algorithm Using Multiple Features

Cited by 4 publications

References 7 publications

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Mask R-CNN for Moving Shadow Detection and Segmentation

Near-infrared shadow detection based on HDR image

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