Abstract
More and more network cameras are now working over distributed networks, offering the capability of remote intelligent video surveillance. In this paper, we bring forward an original particle filter tracking algorithm named labelled particle filter which describes each image patch with a binary label. Based on the imaging theory of thermography, moving objects, such as pedestrians and automobiles, usually have higher intensities compared with the background in a gray-level pseudocolor mode. Thus an image patch can be classified into two categories according to its intensity distribution, and we can use a one-bit binary label, positive or negative, to describe the attribute of image patch. Therefore, the candidate target template is established only if the label of candidate target matches the label of reference target, and the computational complexity is reduced consequently. Experiments are conducted to show that the proposed algorithm can handle real-time object tracking with less time cost while maintaining high tracking accuracy.
Highlights
Visual tracking, which provides cohesive information about the target objects, has been extensively used in computer vision, especially in intelligent video surveillance for antiterrorism and civil protection [1]
With the tendency that thermal infrared imagers are being widely applied in distributed networks, robust object tracking can be achieved for the desirable property, as the background is relatively simple in thermal infrared video
Numerous algorithms have been proposed for addressing these issues, including the Kalman filter techniques [2,3,4], the mean shift algorithms [5,6,7], and the particle filtering methods [8,9,10]
Summary
Visual tracking, which provides cohesive information about the target objects, has been extensively used in computer vision, especially in intelligent video surveillance for antiterrorism and civil protection [1]. The particle filter, as a stochastic method, is the main approach to handle the object tracking tasks, due to its desirable performance in nonlinear and nonGaussian state estimation [11]. It generates a set of random samples, which are propagated and updated recursively in order to approximate the state probability density function of the system. We propose an original object tracking algorithm with the name of labelled particle filter (abbreviated as LPF), on the basis of the particle filter.
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