A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple o...

Eigenface or Principal Component Analysis (PCA) methods have demonstrated their success in face recognition, detection, and tracking. The representation in PCA is based on the second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics (HOS) have been used as a more in...

The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face tracking for a perceptual user interface. In this paper, we review the CamShift Algorithm and extend a default implementation to allow tracking in an arbitrary number and type of feature spaces. In order to compute the new pr...

Multi-object tracking technology plays a crucial role in many applications, such as autonomous vehicles and security monitoring. This paper proposes multi-object framework based on the multi-modal information of 3D point clouds color images. At each sampling instant, cloud image acquired by LiDAR camera are fused into cloud, where objects detected Point-GNN method. And, novel height-intensity-d...

This paper proposes a means of using facial color to enhance conventional face detectors. To a detect face rapidly, the proposed approach adopts a color filtering based efficient region scanning method. The scanning method skips over regions that do not contain possible faces, based on a facial color membership function. By integrating the proposed face detector with a kernel based object track...

In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background....

-The researchers has attracted on object tracking research. There are many tracking algorithm, The purpose of object tracking algorithm is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. Preceding steps for tracking an object in sequence of images are the object detection and object classification. To check existence and to locate t...

Kernels are executable code segments and kernel fusion is a technique for combing the segments in a coherent manner to improve execution time. For the first time, we have developed a technique to fuse image processing kernels to be executed on GPGPUs for improving execution time and total throughput (amount of data processed in unit time). We have applied our techniques for feature tracking on ...