Object tracking is the process of locating moving objects in the consecutive video frames. Real time object tracking is a challenging problem in the field of computer vision, motion-based recognition, automated surveillance, traffic monitoring, augmented reality, object based video compression etc. In this paper kernel based object tracking using color histogram technique has been applied for d...

Moving Object Tracking is one of the challenging problems in the field of computer vision, surveillance, traffic monitoring, video compression etc. The goal of object tracking is to locate a moving object in consecutive video frames. Normally a video tracking system combines three stages of data treating; object extraction, object recognition & tracking, and decisions about activities. This pap...

This paper presents ARGOS, the first system that can automatically uncover the semantics of kernel objects directly from a kernel binary. Based on the principle of data use reveals data semantics, it starts from the execution of system calls (i.e., the user level application interface) and exported kernel APIs (i.e., the kernel module development interface), and automatically tracks how an inst...

Object tracking is very essential task in many application of computer vision such as surveillance, vehicle navigation, autonomous robot navigation, etc. It contains detection of interesting moving objects and tracking of such objects from frame to frame. Its main task is to find and follow a moving object or multiple objects in image sequences. Normally there are three stages of video analysis...

Object tracking is a very essential task in many applications of computer vision such as surveillance, vehicle navigation, autonomous robot navigation, etc. It contains detection of interesting moving objects and tracking of such objects from frame to frame. Its main task is to find and follow a moving object or multiple objects in image sequences. Normally there are three stages of video analy...

This paper presents a special form of color correlogram as representation for object tracking and carries out a motion observability analysis to obtain the optimal simplified correlogram in a kernel based tracking framework. Compared with the color histogram, where the position information of each pixel is ignored, a simplified color correlogram (SCC) representation encodes the spatial informat...

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

A modified movable object tracking algorithm which uses the flexible Metric Distance Transform kernel and FCM Classifier is proposed and tested. The target shape which defines the dn Distance Transform is found based on conventional statistical parameters as feature vector extraction and Fuzzy C-Mean (FCM) classifier to differentiate tracked target from background. This replaces the more usual ...

Visual object tracking has many applications related to computer vision. Recently, correlation filter based trackers have been ranked as the highest performers in this field. However, handling chronic problems such as occlusion, deformation, and scale variations is difficult with such trackers. These problems are solved by many other researches that employ other features and improve an appearan...

This paper presents a particle swarm optimization (PSO) based approach for multiple object tracking based on histogram matching. To start with, gray-level histograms are calculated to establish a feature model for each of the target object. The difference between the gray-level histogram corresponding to each particle in the search space and the target object is used as the fitness value. Multi...