A New Framework for Background Subtraction Using Multiple Cues

Noh, SeungJong; Jeon, Moongu

doi:10.1007/978-3-642-37431-9_38

Cited by 81 publications

(55 citation statements)

References 25 publications

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“…After the SRMs are built, we use a semi-automated algorithm for dataset generation [5] whereby training samples are effectively collected from a scene with minimal human intervention by using background subtraction [20]. Next, to assign the gathered samples to SRMs (Fig.…”

Section: Constructing Training-sets For Each Local Regionmentioning

confidence: 99%

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Vehicle Detection Using Local Size-Specific Classifiers

Noh

Jeon

2016

IEICE Trans. Inf. & Syst.

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SUMMARYAs the number of surveillance cameras keeps increasing, the demand for automated traffic-monitoring systems is growing. In this paper, we propose a practical vehicle detection method for such systems. In the last decade, vehicle detection mainly has been performed by employing an image scan strategy based on sliding windows whereby a pre-trained appearance model is applied to all image areas. In this approach, because the appearance models are built from vehicle sample images, the normalization of the scales and aspect ratios of samples can significantly influence the performance of vehicle detection. Thus, to successfully apply sliding window schemes to detection, it is crucial to select the normalization sizes very carefully in a wise manner. To address this, we present a novel vehicle detection technique. In contrast to conventional methods that determine the normalization sizes without considering given scene conditions, our technique first learns local region-specific size models based on scenecontextual clues, and then utilizes the obtained size models to normalize samples to construct more elaborate appearance models, namely local sizespecific classifiers (LSCs). LSCs can provide advantages in terms of both accuracy and operational speed because they ignore unnecessary information on vehicles that are observable in faraway areas from each sliding window position. We conduct experiments on real highway traffic videos, and demonstrate that the proposed method achieves a 16% increased detection accuracy with at least 3 times faster operational speed compared with the state-of-the-art technique.

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Section: Constructing Training-sets For Each Local Regionmentioning

confidence: 99%

“…To improve computational efficiency, we first extract ROIs in a frame using background subtraction [20] and then apply a search window to each ROI. More explicitly, for a given ROI F , we first find the most spatially closely related SRM by:…”

Section: Initial Detectionmentioning

confidence: 99%

Vehicle Detection Using Local Size-Specific Classifiers

Noh

Jeon

2016

IEICE Trans. Inf. & Syst.

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“…This allows LBSP to capture both texture and intensity changes. Noh and Jeon (2012) propose to improve the SILTP (Liao et al, 2010) thanks to a codebook method. The derived descriptor gain in robustness when segmenting moving objects from dynamic and complex backgrounds.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, concluding remarks and some perspectives are drawn in Section 5. (Ojala et al, 2002) • 256 Modified LBP (Heikkilä and Pietikäinen, 2006) • • 256 CS-LBP (Heikkilä et al, 2009) • 16 STLBP (Shimada and Taniguchi, 2009) • • 256 εLBP • 256 Adaptive εLBP • 256 SCS-LBP (Xue et al, 2010) • • 16 SILTP (Liao et al, 2010) • 256 CS-LDP (Xue et al, 2011) • 16 SCBP (Xue et al, 2011) • 64 OCLBP (Lee et al, 2011) • 1536 Uniform LBP (Yuan et al, 2012) • 59 SALBP (Noh and Jeon, 2012) • 128 SLBP-AM (Yin et al, 2013) • • 256 LBSP (Bilodeau et al, 2013) • • 256 iLBP (Vishnyakov et al, 2014) • 256 CS-SILTP (Wu et al, 2014) • • 16 XCS-LBP (in this paper)…”

Section: Introductionmentioning

confidence: 99%

An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos

Silva

Bouwmans

Frélicot

2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

105

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Abstract:In this paper, we propose an eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) descriptor for background modeling and subtraction in videos. By combining the strengths of the original LBP and the similar CS ones, it appears to be robust to illumination changes and noise, and produces short histograms, too. The experiments conducted on both synthetic and real videos (from the Background Models Challenge) of outdoor urban scenes under various conditions show that the proposed XCS-LBP outperforms its direct competitors for the background subtraction task.

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“…Zhao et al [23] proposed a background modeling method for motion detection in dynamic scenes based on type-2 fuzzy Gaussian mixture model [24] and Markov random field (MRF) [25]. In [26], authors introduced a background subtraction framework based on texture feature. Furthermore, color cues are clustered by the codebook scheme in order to refine the texture-based detection.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Pedestrians in Videos

Yan¹,

Ren²,

Zhao

et al. 2017

Cogn Comput

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In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by a two-stage background subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge-based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. To evaluate the proposed method, a publicly available color-thermal benchmark dataset Object Tracking and Classification in and Beyond the Visible Spectrum is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. Promising results have shown that the proposed fusion-based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem.

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A New Framework for Background Subtraction Using Multiple Cues

Cited by 81 publications

References 25 publications

Vehicle Detection Using Local Size-Specific Classifiers

Vehicle Detection Using Local Size-Specific Classifiers

An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos

Cognitive Fusion of Thermal and Visible Imagery for Effective Detection and Tracking of Pedestrians in Videos

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