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In this paper, we propose a Local Soft Similarity based on Soft Cosine Measure (L3SCM) and then we incorporate it into visual tracking framework. Firstly, we present the soft cosine measure that measures the soft similarity between two vectors of features by taking into consideration similarities of pairs of features. Secondly, we apply this soft similarity in the observation model component of the...
Tracking a moving object in image sequences from a stationary video camera is a crucial task for surveillance applications. This paper proposes a hybrid technique that combines Kalman Filter (KF) and the Support Vector Machines (SVM). First, the moving target is determined according to the user's interest, and then the system state model of the KF algorithm is constructed. Second, a set of patterns...
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...
The moving objects detection is considered as an important factor for many video surveillance applications. To assure a best detection a background model should be generated. This paper proposes a background modeling approach. To generate this model, we use both pixel-based and block-based processes to classify background pixels from those belong to the foreground. After that, to minimize the noise...
Moments and moment invariants are the most useful tools in pattern recognition. Recently, the Conventional Disc-Harmonic Moments (CDHMs) are used to describe binary and gray scale images. In order to deal with color images in a holistic manner, these CDHMs are generalized as Quaternion Disc-Harmonic Moments (QDHMs) by using the quaternion algebra. Then the Rotation, Scaling and Translation (RST) invariants...
To detect the moving objects in a video sequence based on background subtraction approaches, a background model should be estimated at the first time before subtract it from each image of the sequence and then segmenting the moving objects. In this paper, we present a new approach based on the combining of the background subtraction and the structure–texture decomposition (BS–STD). First, each gray-level...
Motion tracking is one of the richest research fields in computer vision. Indeed, numerous algorithms have been implemented for object tracking. In this paper we briefly present the principles of three recent methods treating the motion tracking: the discriminative sparse similarity map (DSS map), the probability continuous outlier model (PCOM) and the L2 regularized least square (L2-RLS). And then...
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...
To detect the moving objects in a video sequence based on background subtraction approaches, a background model should be generated at the first time before subtract it from each image of the sequence and then segmenting the moving objects. But this detection can be difficult when the environment is influenced by illumination and weather changes. In The goal to solve the problem of environmental illumination...
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