Abstract
Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.
Highlights
Video target tracking is an important research field in computer vision for its wide range of application demands and prospects in many industries, such as military guidance, visual surveillance, visual navigation of robots, humancomputer interaction and medical diagnosis [1,2,3], and so forth
Two main tasks needs to be completed by moving target tracking during the processing procedure: the first one is target detection and classification which detects the location of relevant targets in the image frames; the second one is the relevance of the target location of consecutive image frames, which identifies the target points in the image and determines their location coordinates, to determine the trajectory of the target as time changes
Comaniciu and Ramesh [6] gave a strict proof of the convergence of the algorithm and proposed a mean shift based on tracking method
Summary
Video target tracking is an important research field in computer vision for its wide range of application demands and prospects in many industries, such as military guidance, visual surveillance, visual navigation of robots, humancomputer interaction and medical diagnosis [1,2,3], and so forth. Mean shift keeps single hypothesis and is computationally efficient. It may run into trouble when similar targets are presented in background or occlusion occurs. Another common approach is the use of the Kalman filter [7]. This approach is based on the assumption that the probability distribution of the target. In video target tracking, tracking targets in real world rarely satisfy Gaussian assumptions required by the Kalman filter in that background clutter may resemble a part of foreground features. Due to particle filters’ non-Gaussian, non-linear assumption and multiple hypothesis property, they have been successfully applied to video target tracking [9]
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