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An autonomous, efficient and effective object tracking algorithm was required to autonomously identify and track incoming targets. Then controlling a pan-tilt mounted with the sensing camera to accommodate the target within the camera's field of view and controlling a weapon mounted on the second mechanical pan tilt to lock the target and follow it efficiently and accurately. A hybrid algorithm is...
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...
Small target detection in infrared imagery with complex background is always an important task in infrared target tracking system. Complex clutter background usually results in serious false alarm because of low contrast of infrared imagery. In this paper, a composite kernel regression method is proposed for infrared small target detection. In the proposed method, a nonlinear regression model is firstly...
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...
Visual tracking in the real world is challenging with unavoidable background interference, target orientation variations and scale changes. Spatial information needs to be exploited to increase robustness; however, current methods such as ldquoSpatiogramrdquo suffer from the large complexity of spatial covariance calculation. Recently, joint distribution representation has been used to estimate target...
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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