Omar Elharrouss

Moving objects detection based on thresholding operations for video surveillance systems

Motion detection based on background subtraction approaches require a background model generation before extracting the moving objects. This extraction consists to subtract the static scene from the current image. The result of subtraction will be segmented in order to represent the moving object by a binary image using a threshold. In this paper a new background subtraction approach is presented. Firstly, each gray-level image of the sequence will be decomposed on two components, structure and texture/noise by applying the Osher and Vese algorithm. The structure component of each image will be taken to generate the background model. The background model development uses a threshold in order to decide if a pixel belongs to the background or to the foreground. The absolute difference is used to subtracting the background before compute the binary image of the moving objects using a proposed threshold selection operation. The experimental results demonstrate that our approach is effective and accurate moving objects detection comparing with the results of two existing methods.

Moving object detection zone using a block-based background model

Background modelling is a critical case for background-subtraction-based approaches and also for a wide range of applications. The background generation becomes difficult when the scene is complex or an object stays for a long time in the scene. Here, the authors propose a block-based background initialisation, using the sum of absolute difference (SAD), and modelling, using a block-based entropy evaluation, with a low computational cost which making them feasible for embedded platform. In general, many background-subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modelling approach analyses the illumination change problem. The moving object detection mask is developed using a threshold selected by computing the mean of the SAD between the blocks background and the blocks of the current frame. From the qualitative and quantitative results obtained by the authors approach compared with some existing methods, the authors approach is effective for background generation and moving objects detection.

Moving object detection using a background modeling based on entropy theory and quad-tree decomposition

Abstract. A particular algorithm for moving object detection using a background subtraction approach is proposed. We generate the background model by combining quad-tree decomposition with entropy theory. In general, many background subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modeling approach analyzes the illumination change problem. After performing the background subtraction based on the proposed background model, the moving targets can be accurately detected at each frame of the image sequence. In order to produce high accuracy for the motion detection, the binary motion mask can be computed by the proposed threshold function. The experimental analysis based on statistical measurements proves the efficiency of our proposed method in terms of quality and quantity. And it even outperforms substantially existing methods by perceptional evaluation.

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to pre-processing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

Visual object tracking based on disc-harmonic moments features invariants (Withdrawal Notice).

Publishers Note: This paper, originally published on 14-May was withdrawn because it was not presented at the conference.

Moving object detection with an adaptive background model

Background modeling is a critical case for background-subtraction-based approaches and also for a wide range of applications. A background generation becomes difficult when the scene is complex or an object stay for more than half of the time in the scene. In this paper, we propose a block-based scene background initialization and modeling with low computational cost which making them feasible for Embedded Platform. In general, many background subtraction approaches are sensitive to sudden illumination changes in the scene and does not update the background model properly over time. The proposed background modeling approach analyzes the illumination change problem. From the quantitative evaluation selected through a suite of metrics, and compared results obtained by some existing methods, our approach is effective for background generation.