Results 21 to 30 of about 17,012 (262)
IEEE Access, 2021 Background subtraction (BGS) is a fundamental video processing task which is a key component of many applications. Deep learning-based supervised algorithms achieve very good performance in BGS, however, most of these algorithms are optimized for either ...
M. Ozan Tezcan +2 more
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EURASIP Journal on Audio, Speech, and Music Processing, 2021 Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large amounts of open-source datasets and ...
Rajat Hebbar +8 more
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WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection
Sensors, 2019 The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions.
Wenhui Li, Jianqi Zhang, Ying Wang
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Pedestrian Detection with Semantic Regions of Interest
Sensors, 2017 For many pedestrian detectors, background vs. foreground errors heavily influence the detection quality. Our main contribution is to design semantic regions of interest that extract the foreground target roughly to reduce the background vs.
Miao He, Haibo Luo, Zheng Chang, Bin Hui
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Unsupervised learning of foreground object detection
CoRR, 2018 International Journal of Computer Vision (IJCV ...
Ioana Croitoru +2 more
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EAI Endorsed Transactions on Energy WebBackground: Traditional foreground detection methods for new energy vehicles using the ViBe algorithm often suffer from ghosting effects, which can obscure the accurate detection of moving targets.
Lei Gu
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An Edge-Based Approach for Robust Foreground Detection [PDF]
, 2011 Foreground segmentation is an essential task in many image processing applications and a commonly used approach to obtain foreground objects from the background. Many techniques exist, but due to shadows and changes in illumination the segmentation of foreground objects from the background remains challenging.
Grünwedel, Sebastian +2 more
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Flow-Process Foreground Region of Interest Detection Method for Video Codecs
IEEE Access, 2017 Detecting the foreground region of interest (ROI) for video sequences is an important issue both for video codecs and monitoring systems. In this paper, we propose a flow-process-based method to detect foreground ROI using four steps: global motion ...
Zhewei Zhang +4 more
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Moving Object Detection Based on Non-Convex RPCA With Segmentation Constraint
IEEE Access, 2020 Recently, robust principal component analysis (RPCA) has been widely used in the detection of moving objects. However, this method fails to effectively utilize the low-rank prior information of the background and the spatiotemporal continuity prior of ...
Zixuan Hu +5 more
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An improved ViBe algorithm based on adaptive detection of moving targets
Journal of Measurement Science and Instrumentation, 2020 There exists a Ghost region in the detection result of the traditional visual background extraction（ViBe） algorithm, and the foreground extraction is prone to false detection or missed detection due to environmental changes.
WANG Wei +2 more
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