Interactive deep learning method for segmenting moving objects

Wang, Yi; Luo, Zhiming; Jodoin, Pierre-Marc

doi:10.1016/j.patrec.2016.09.014

Cited by 292 publications

(210 citation statements)

References 10 publications

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“…The current methods of detecting moving objects are mostly based on the background subtraction so far because of their fast processing speed [4,5]. However, thanks to the recent advances in object detection methods such as YOLO [6] and SSD [7], which work well in real-time, we may attempt to use these techniques for the the detection of moving objects.…”

Section: Introductionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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Section: Introductionmentioning

confidence: 99%

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

et al. 2018

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“…Recently, several authors [40,86] successfully started to use them for background modeling, and for all categories of videos. For example, one could consider integrating uncertainty management into these techniques, using fuzzy sets.…”

Section: Discussionmentioning

confidence: 99%

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

et al. 2018

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Abstract:Moving foreground detection is a very important step for many applications such as human behavior analysis for visual surveillance, model-based action recognition, road traffic monitoring, etc. Background subtraction is a very popular approach, but it is difficult to apply given that it must overcome many obstacles, such as dynamic background changes, lighting variations, occlusions, and so on. In the presented work, we focus on this problem (foreground/background segmentation), using a type-2 fuzzy modeling to manage the uncertainty of the video process and of the data. The proposed method models the state of each pixel using an imprecise and adjustable Gaussian mixture model, which is exploited by several fuzzy classifiers to ultimately estimate the pixel class for each frame. More precisely, this decision not only takes into account the history of its evolution, but also its spatial neighborhood and its possible displacements in the previous frames. Then we compare the proposed method with other close methods, including methods based on a Gaussian mixture model or on fuzzy sets. This comparison will allow us to assess our method's performance, and to propose some perspectives to this work.

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“…In the past few years, many CNN-based background subtraction algorithms [26], [27], [28], [29], [30] have been proposed. The first scene specific CNN-based background subtraction method is proposed in [26].…”

Section: Related Workmentioning

confidence: 99%

Background Subtraction With Real-Time Semantic Segmentation

et al. 2019

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Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background models and segments foreground objects. The realtime semantic segmenter S is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary foreground/background (FG/BG) mask Bt. At the same time, the real-time semantic segmenter S extracts the object-level semantics St. Then, some specific rules are applied on Bt and St to generate the final detection Dt. Finally, the refined FG/BG mask Dt is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves stateof-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.

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Interactive deep learning method for segmenting moving objects

Cited by 292 publications

References 10 publications

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Background Subtraction Based on a New Fuzzy Mixture of Gaussians for Moving Object Detection

Background Subtraction With Real-Time Semantic Segmentation

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