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This paper presents a new approach which combines the Kernel Density Estimation and Trust Region algorithm for tracking objects in video sequences. Kernel density estimation (KDE) of the object's color distribution is built from the object region and used to generate a probability map for each incoming frame. Tracking is accomplished by localizing blobs in the maps. Compared with color histograms...
Many computer vision algorithms such as object tracking and event detection assume that a background model of the scene under analysis is known. However, in many practical circumstances it is unavailable and must be estimated from cluttered image sequences. We propose a sequential technique for background estimation in such conditions, with low computational and memory requirements. The first stage...
We present an unsupervised method to estimate the camera orientation angle on monocular video scenes in the H.264 compressed domain. The method is based on the presence of moving objects in the scene. We start by estimating the global camera motion based on the motion vectors present in the stream, detect and track moving objects and estimate their relative distance to the camera by analyzing the...
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by non-parametric kernel density estimation. The major contribution is a novel bidirectional learning framework for discrimination between the object and background. It has the following advantages: 1) it embeds background information, unlike...
In this paper, we present a technique of detection and tracking of one or possibly more moving objects by implementing a "vector bank" into an H.264 based system-on-a-chip design architecture as a vision sensor in aviation systems, such as unmanned aerial vehicles (UAV). Most of the existing target-tracking algorithms are based on software solutions. The introduction of H.264 encoding chips...
This paper proposed a new method for multi-target tracking in video sequences by combining two trackers, sum-of-squared differences (SSD) and kernel particle filter (KPF). In our work, the idea of Object Likelihood Value of pixel is proposed. Instead of using direct propagation resample result from the previous sample set, a weighted SSD displacement is used for reinitializing and resample before...
A new approach to adapt the kernel scale and orientation in real-time tracking is proposed. The iterative procedure, mean shift, is the key point to find the most credible target location. Though it performs well in some bad conditions, such as camera motion, partial occlusions, and background clutters, it has limited performance on tracking the object with the changing size. In this paper, the adaptive...
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