Foreground detection in camouflaged scenes

Li, Shuai; Florêncio, Dinei; Zhao, Yaqin; Cook, Chris; Li, Wanqing

doi:10.1109/icip.2017.8297083

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…In the same direction, Li et al proposed a texture guided weighted voting (tgwv) method detect foreground object in camouflaged scenes [20]. This method employed the stationary wavelet transform to decompose the image into frequency bands.…”

Section: Texture Featuresmentioning

confidence: 99%

Camouflaged Object Detection and Tracking: A Survey

Mondal

2020

Int. J. Image Grap.

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Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

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Section: Texture Featuresmentioning

confidence: 99%

Camouflaged Object Detection and Tracking: A Survey

Mondal

2020

Int. J. Image Grap.

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“…CAMO-UOW dataset was recorded in 2017 to address the problem of camouflaged moving foreground detection in real scenes [174], [175]. It comprises of 10 videos having 3,517 total video frames.…”

Section: ) Camo-uow Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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“…As shown in Figure 2 , different from salient object detection (SOD), i.e., detecting objects of potential human interest, COD focuses on targets that are less likely to capture human attention or attempt to deceive visual perception systems in an adversarial manner. In early studies, COD was often approached as foreground detection, which utilizes the hand-crafted features computed by edges, brightness, corner points, texture, or temporal information [ 4 ] to separate the camouflaged object and the background [ 5 , 6 , 7 ]. However, the hand-crafted features are incapable of detecting all the sophisticated camouflage strategies in the real application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

Huang

Zhang

et al. 2021

Sensors

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The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder–decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.

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Foreground detection in camouflaged scenes

Cited by 20 publications

References 20 publications

Camouflaged Object Detection and Tracking: A Survey

Camouflaged Object Detection and Tracking: A Survey

A Comprehensive Survey of Video Datasets for Background Subtraction

Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing

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