On line background modeling for moving object segmentation in dynamic scenes

Hammami, Mohamed; Jarraya, Salma Kammoun; Ben-Abdallah, Hanêne

doi:10.1007/s11042-011-0935-6

Cited by 17 publications

(10 citation statements)

References 30 publications

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“…We adopt our fast and accurate moving object detection method described in [4] There is matching (RMatch = 1) between two regions in case of at least one descriptor match (Des Match >0). Decision to select matched descriptors from .…”

Section: 1spatial Analysis (As)mentioning

confidence: 99%

A Method for Tracking Road Objects

Jarraya¹

2018

IJMA

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In this paper, we present a new road traffic monitoring approach for a highway control and management system called RoadGuard. This system copes with several challenges in this type of applications in order to count and track road objects robustly. It adopts a novel approach for tracking road objects, first, to determine continuously their positions on the road, and then it uses vehicle positions to estimate their trajectories. The trajectory analysis provides vital information to control and manage highway traffic. In this paper, our main contribution is a tracking method based on a coherent strategy where both region and object information are used to establish objects correspondence over time. Our method operates in two phases: a spatial analysis that uses a multilevel region descriptors matching in order to identify object interactions and particular object states; and a continuous temporal analysis applied to cope with track management issues. As demonstrated experimentally, the proposed method can detect, track and count road objects accurately in highway videos that include several constraints. In addition, it produces effective and stable road objects tracking.

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Section: 1spatial Analysis (As)mentioning

confidence: 99%

A Method for Tracking Road Objects

Jarraya¹

2018

IJMA

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“…To detect moving objects, we consider a background modeling-based approach developed within our research team [24]. This method is robust to many challenges such as sudden and gradual illumination changes, background component changes, ghost, and foreground speed.…”

Section: ) Trajectory Extractionmentioning

confidence: 99%

Abnormal Events Detection Based on Trajectory Clustering

Ghrab

Fendri

Hammami

2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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Trajectories of moving objects provide crucial clues for video event analysis especially in surveillance applications. In this paper, we proposed a novel approach for detecting abnormal events in video surveillance. Our approach is based on trajectory analysis involving two phases. In the first phase, we extracted clusters of normal events through an agglomerative hierarchical clustering of saved trajectories that were of different lengths, of different local time shifts and containing noise. Then, for each cluster a model was established. In the second phase, we aimed to classify a new event as normal or abnormal one. To achieve this objective, a comparison was performed with the extracted clusters' models thereby reducing the complexity and accelerating the classification process. Experiments were conducted to demonstrate the efficacy and the performance of our approach.

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“…Mohamed Hammami etc. [4] presented a background modeling method which is moving target segmentation based on dynamic matrix and temporal analysis. Bohyung Han etc.…”

Section: Related Workmentioning

confidence: 99%

Video Background Subtraction Algorithm for a Moving Camera

Guo

Fan

2015

IJMUE

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At present, the video background extraction algorithm of static scene has been nearly mature. However, video background extraction in dynamic scenes remains a challenge. In order to solve this problem, this paper proposes a dynamic scenes video background extraction algorithm. Here, our dynamic scene is based on camera movement. Firstly, we detect saliency target of video frame according to context information and do a processing of fuzzy enhancement. Meanwhile, we analyze flow filed by SIFT Flow method to do a nonlinear fusion with fuzzy enhancement result. Up to now, we can obtain moving target .Because of other gray information in the foreground affect target extraction, so we have to do a process of binarization and find out bounding box. After these preparations, we will track moving object with real-time algorithm. Finally, we use KNN algorithm to get accurate moving targets. Experiment results show that the proposed method for dynamic scenes video background extraction could get better results.

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On line background modeling for moving object segmentation in dynamic scenes

Cited by 17 publications

References 30 publications

A Method for Tracking Road Objects

A Method for Tracking Road Objects

Abnormal Events Detection Based on Trajectory Clustering

Video Background Subtraction Algorithm for a Moving Camera

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