Complementary background models for the detection of static and moving objects in crowded environments

Evangelio, Rubén Heras; Sikora, Thomas

doi:10.1109/avss.2011.6027297

Cited by 41 publications

(39 citation statements)

References 8 publications

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“…Haines et al [58] also propose an automatic mode selection method, but with a Dirichlet process. A splitting GMM that relies on a new initialization procedure and a mode splitting rule was proposed in [46,48] to avoid over-dominating modes and resolve problems due to newly static objects and moved away background objects while a multi-resolution block-based version was introduced in [146]. The GMM approach can also be expanded to include the generalized Gaussian model [4].…”

Section: Overview and Benchmarking Of Motion Detection Methodsmentioning

confidence: 99%

“…This includes the well-known methods by Stauffer and Grimson [156], a self-adapting GMM by KaewTraKulPong [74], the improved GMM method by Zivkovic and Heijden [202], the multiresolution block-based GMM (RECTGAUSS-Tex) by Dora et al [146], GMM method with a Dirichlet process (DPGMM) that automatically estimated the number of Gaussian modes [58] and the SGMM and SGMM-SOD methods by Evangelio et al [48,46] which rely on a new initialization procedure and novel mode splitting rule. We also included a recursive per-pixel Bayesian approach by Porikli and Tuzel [138] which shows good robustness to shadows according to [54].…”

Section: Benchmarks Motion Detection Methodsmentioning

confidence: 99%

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Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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Section: Overview and Benchmarking Of Motion Detection Methodsmentioning

confidence: 99%

Section: Benchmarks Motion Detection Methodsmentioning

confidence: 99%

Background Subtraction on Embedded Hardware

Jodoin¹,

Pierard

Wang³

et al. 2014

Background Modeling and Foreground Detection for Video Surveillance

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“…Dual BS approaches rely on fast and slow updated BS algorithms to identify the stationary objects [2] [11]. However, BS failures require additional postprocessing, such as edge features or fast-slow model interaction [12], which avoids detections of background objects that are removed from the scene. Other approaches take advantage of multilayer BS algorithms to model moving objects, stationary objects and background [13].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

Ortego

SanMiguel

Martinez

2015

IEEE Signal Process. Lett.

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract-We present a block-wise approach to detect stationary objects based on spatio-temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short-and long-term operation while overcoming challenging issues.

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“…To determine whether the object exists in the current frame the mechanism described in the work [13] was adapted. It involves a parallel edge analysis of the object (object's mask), the current frame and the background model.…”

Section: Improvement Of the Pbas Algorithmmentioning

confidence: 99%

Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

Kryjak¹,

Komorkiewicz²,

Gorgon³

2014

International Journal of Electronics and Telecommunications

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Abstract-The article presents a hardware implementation of the foreground object detection algorithm PBAS (Pixel-Based Adaptive Segmenter) with a scene analysis module. A mechanism for static object detection is proposed, which is based on consecutive frame differencing. The method allows to distinguish stopped foreground objects (e.g. a car at the intersection, abandoned luggage) from false detections (so-called ghosts) using edge similarity. The improved algorithm was compared with the original version on popular test sequences from the changedetection.net dataset. The obtained results indicate that the proposed approach allows to improve the performance of the method for sequences with the stopped objects. The algorithm has been implemented and successfully verified on a hardware platform with Virtex 7 FPGA device. The PBAS segmentation, consecutive frame differencing, Sobel edge detection and advanced one-pass connected component analysis modules were designed. The system is capable of processing 50 frames with a resolution of 720 × 576 pixels per second.

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Complementary background models for the detection of static and moving objects in crowded environments

Cited by 41 publications

References 8 publications

Background Subtraction on Embedded Hardware

Background Subtraction on Embedded Hardware

Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

Real-time Foreground Object Detection Combining the PBAS Background Modelling Algorithm and Feedback from Scene Analysis Module

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