Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications

Sekkati, Hicham; Laganière, Robert; Mitiche, Amar; Youmaran, R.

doi:10.1109/crv.2012.33

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…They have combined ICA along with particle swarm optimisatin(PSO) in their approach. Another proposition based on contour and ICA based segmentation is proposed by Sekkati et al [20].…”

Section: Related Workmentioning

confidence: 99%

Foreground Extraction Using Hilbert-Schmidt Independence Criterion and Particle Swarm Optimization Independent Component Analysis

Toroghi

Mirzarezaee

Araabi

2020

IJE

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Foreground extraction is one of the crucial subjects in image processing, which drives different applications in industry. The reality behind the continuous research in this area is the various challenging problems we encounter during the separation process of foreground and background images. Among the source separation approaches, the independent component analysis (ICA) is the most prevalent, being involved in different areas of signal separation applications. Despite the improvements being achieved in foreground extraction, the sudden luminance variations and background movements adversely impact the results of techniques in this regard. In this paper, a novel structure called HSIC_ICA is introduced to address the mentioned problem using a modified version of the ICA algorithm which, leverages the Hilbert-Schmidt Independence Criterion (HSIC) instead of the common objective functions. Moreover, the unmixing matrix elements of ICA are extracted through a Particle Swarm Optimization (PSO) evolutionary algorithm in a much faster way. The experimental results clearly show that the proposed method outperforms over the significant works being cited among the references, using Wallflower dataset.

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“…They have combined ICA along with particle swarm optimisatin(PSO) in their approach. Another proposition based on contour and ICA based segmentation is proposed by Sekkati et al [20].…”

Section: Related Workmentioning

confidence: 99%

Foreground Extraction Using Hilbert-Schmidt Independence Criterion and Particle Swarm Optimization Independent Component Analysis

Toroghi

Mirzarezaee

Araabi

2020

IJE

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“…1. Given an input video, we first extract the body silhouette by foreground extraction [9]. Then we find the center of gravity (COG) of the body silhouette.…”

Section: Feature-based Gait Analysismentioning

confidence: 99%

Human gait analysis by body segmentation and center of gravity

Tsao

Liu

Chiu

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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The physiological condition of a person may affect his/her daily behaviour such as gait or posture. For example under the fatigue condition, a person may be used to walk in a slower pace than usual. This paper presents a novel gait analysis approach to detect movement variations such as walking pace or speed change, walking with bending, walking with heavy breath, arm or leg swing change. Based on the geometry of the silhouette, we segment the body to five main parts including head, upper body, lower body, arms and legs. For a specific analysis, we segment the torso to upper and lower body. For the walking pace analysis, we use the leg movement in the lower body to find the max distance in a pace cycle and corresponding pace speed. The angles between the head or upper body and the vertical line are used to detect the walking with bending or walking with breathing. The arm swing angle or pace variation during walking can also be detected. We compare the normal condition with other abnormal condition such as people who have respiratory obstruction leading to heavy breathing, and have stomach ache resulting humpbacked status. These cause the angle of upper body different with normal condition, so we can observe these signals to give a warning notice. Our experiments show that with these fine posture features, we are able to detect a person's gait change. Examples are that a person is humpbacked, or the arm/leg swing and pace distance are in abnormal rhythm. From our gait analysis approach, we observe that when people are in a tired condition, they are used to adopt a static and comfortable pace distance to walk in our experimental results.

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“…They have combined ICA along with particle swarm optimization (PSO)in their approach. Another proposition based on contour and ICA based segmentation is proposed by Hicham Sekkati [2]. Xiao-Ping Zhang [3] presented an approach for video object extraction based on spatio-temporal analysis.…”

Section: Introductionmentioning

confidence: 99%