Robust outdoor human segmentation based on color-based statistical approach and edge combination

Siricharoen, Punnarai; Aramvith, Supavadee; Chalidabhongse, Thanarat H.; Siddhichai, Supakorn

doi:10.1109/icgcs.2010.5543017

Cited by 19 publications

(5 citation statements)

References 8 publications

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“…Traditional COD methods segment camouflaged objects by extracting artificial features such as texture, color, edge, contrast, and 3D convexity in camouflage scenes [11][12][13][14][15]. However, these features are inefficient in complex scenes.…”

Section: Camouflaged Object Detectionmentioning

confidence: 99%

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Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration

Liu

Qiu

et al. 2023

Sensors

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Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fβw, and MAE).

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Section: Camouflaged Object Detectionmentioning

confidence: 99%

“…Traditional camouflaged object detection methods [11][12][13][14][15] typically rely on handcrafted features (textures, colors, edges, etc.) to differentiate camouflaged objects from their surroundings.…”

Section: Introductionmentioning

confidence: 99%

Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration

Liu

Qiu

et al. 2023

Sensors

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“…Camouflaged object detection. Early work for camouflaged object detection (COD) adopted various hand-crafted features, e.g., color [15,31], convex intensity [35], edge [31], and texture [1,17]. Recently, deep convolution neural network achieves great success with the help of largescale camouflaged object datasets [32,19,7].…”

Section: Related Workmentioning

confidence: 99%

Deep Texture-Aware Features for Camouflaged Object Detection

Ren

Zhu

et al. 2021

Preprint

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Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the covariance matrices of feature responses to extract the texture information, designs an affinity loss to learn a set of parameter maps that help to separate the texture between camouflaged objects and the background, and adopts a boundary-consistency loss to explore the object detail structures. We evaluate our network on the benchmark dataset for camouflaged object detection both qualitatively and quantitatively. Experimental results show that our approach outperforms various state-of-the-art methods by a large margin.

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“…But is sensitive to changes in lights and also detects shadow as an object. Thus, to deal with camouflage object edge feature is also added along with color and intensity [22].…”

Section: Literature Surveymentioning

confidence: 99%

Implementation of Modified Mask RCNN

Date*,

Shah

2019

IJITEE

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Detecting camouflage moving object from the video sequence is the big challenge in computer vision. To detect moving object from dynamic background is also very difficult as the background is also detected as moving object. Mask RCNN is a deep neural network which solves the problem of separation of instances of same object in machine learning or computer vision. Thus, it separates different objects in video. It is the extension of faster RCNN in which an extra branch is added to create an object mask simultaneously along with bounding box and classifier. After giving input, Mask RCNN gives the rectangle around the object, class to which object belong and object mask. This article introduces Mask RCNN algorithm along with some modifications for target detection from dynamic background and also for camouflage handling. After target object detection, contrast limited adaptive histogram equalization is applied. Morphological operations are used to improve results. For both challenges quantitative and qualitative measures were obtained and compared with the existing algorithms. Our method efficiently detects the moving object from input sequence and gives best results in both situations.

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Robust outdoor human segmentation based on color-based statistical approach and edge combination

Cited by 19 publications

References 8 publications

Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration

Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration

Deep Texture-Aware Features for Camouflaged Object Detection

Implementation of Modified Mask RCNN

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