In this paper, we present a robust real-time surveillance system of image object tracking for Pan-Tilt-Zoom(PTZ) camera. For image object tracking, we use the CAM shift tracking algorithm based on the HSI color image distribution of detected moving objects. CAM shift algorithm is very efficient for real-time tracking because of its fast and robust performance. In this system, C++ and OpenCV env...

Video Tracking is the process of locating a moving object over time using a camera. The objective of video tracking is to associate target objects in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the frame rate. Another situation that increases the complexity of the problem is when the tracked object changes orientation over t...

This paper discusses an efficient and effective approach for identifying and tracking of moving object from a video. A video is captured by stationary camera. Moving object tracking and detection from video sequences has applications in several areas such as automatic video surveillance, motion-based recognition, video indexing, human-computer interaction, traffic monitoring, and vehicle naviga...

In this paper, we present an image object tracking system for GPGPU based CAMshift algorithm. For image object tracking, we use the parallel CAMshift tracking algorithm based on the HSV color image distribution of detected moving objects. In this, RGB-to-HSV color conversion, image masking such as open and close operation for image morphology, and computing of centroid are executed in parallel....

Many surveillance algorithms use both background modeling to detect moving objects and object tracking to analyze the motion patterns of the objects detected. In our case, Expectation Maximization (EM) is used to model the background and detect moving objects. Tracking is based on the objects color histogram. Using EM we can calculate the probability that a pixel value belongs to the background...

This paper presents an efticient technique for real-time tracking of a single moving object in terrestrial scenes using a stationary camera. The tracking algorithm is based on the Linear Prediction (LP) solved by the Maximum Entropy Method (MEM). It attempts to predict the centroid of the moving object in the next frame, based on several past centroid measurements. Using a second order of the L...

Object segmentation and tracking are important techniques in video applications. In this paper, we present a novel system for active contour tracking of moving objects in video sequences. Our method includes preprocessing to identify an initial object contour, and object contour segmentation to refine the contour of the moving object. The edge flows and ant colony optimization are incorporated ...