Abstract
To boost the robustness of the traditional particle-filter-based tracking algorithm under complex scenes and to tackle the drift problem that is caused by the fast moving target, an improved particle-filter-based tracking algorithm is proposed. Firstly, all of the particles are divided into two parts and put separately. The number of particles that are put for the first time is large enough to ensure that the number of the particles that can cover the target is as many as possible, and then the second part of the particles are put at the location of the particle with the highest similarity to the template in the particles that are first put, to improve the tracking accuracy. Secondly, in order to obtain a sparser solution, a novel minimization model for an Lp tracker is proposed. Finally, an adaptive multi-feature fusion strategy is proposed, to deal with more complex scenes. The experimental results demonstrate that the proposed algorithm can not only improve the tracking robustness, but can also enhance the tracking accuracy in the case of complex scenes. In addition, our tracker can get better accuracy and robustness than several state-of-the-art trackers.
Highlights
Target tracking has always been a popular research direction in the field of computer vision as it has important applications in scene monitoring, behavior analysis, autopilot, robot, and so forth [1,2,3]. the visual tracking technology has made considerable progress, and a large number of excellent tracking algorithms have been proposed [4,5,6,7,8,9,10,11], there are still a series of unpredictable challenges, such as occlusion, motion blur, pose and shape change, illumination change, scale variation, and so on
To improve the tracking speed and accuracy at the same time, Bao et al [8] improved the algorithms that were proposed by the authors of [6,7], through adding the L2 regularization term on the coefficients that were associated with the trivial templates in the L1 norm minimization model, and used the accelerated proximal gradient (APG) method to accelerate the speed of solving sparse coefficients
To improve the tracking accuracy and robustness, we propose three improvements on the basis of the L1 -APG proposed by Bao et al [8] as follows: (1) intelligent particle filter; (2) the minimization model for Lp tracker; and (3) an adaptive multi-feature fusion strategy
Summary
Target tracking has always been a popular research direction in the field of computer vision as it has important applications in scene monitoring, behavior analysis, autopilot, robot, and so forth [1,2,3]. Compared with solving the L1 norm minimization model, solving the Lp minimization model can usually gain sparser and more accurate solutions Considering this advantage, the Lp norm is applied so as to solve the sparse representation coefficients, and a novel minimization model for an Lp tracker is proposed to improve the tracking accuracy in this paper. Considering that a feature generally only adapts to a certain type of scene, the advantage of the complementary features can be used, that is, by combining multiple features to represent the target, in order to improve the robustness of the tracking algorithm to complex scenes.
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