Abstract
The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking's accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper's algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target's saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.
Highlights
Target tracking has attracted a lot of attention in computer vision due to its fundamental importance for many vision applications such as visual surveillance, traffic safety monitoring, and abnormal activity detection
We have proposed a novel combination of adaptive support vector machine (ASVM) based on support vector machine (SVM) and visual saliency feature extraction algorithm for the moving target tracking
In order to evaluate the proposed algorithm in the specific performance, the correct rate of the paper from the training, testing accuracy, and CPU execution time of three elements had been compared with ASVM and online incremental learning algorithms in [12]
Summary
Target tracking has attracted a lot of attention in computer vision due to its fundamental importance for many vision applications such as visual surveillance, traffic safety monitoring, and abnormal activity detection. The applicability of the techniques in general scenarios, is still very limited due to practical difficulties: appearance variations (e.g., illumination, viewpoint, and background changes), occlusions, complex backgrounds, and so forth. The new algorithm has utilized ASVM as the tracking algorithm’s framework, and the use of visual saliency has measured the model to calculate the target saliency features. They have included many common characteristics as the target for model representation in order to overcome the use of the single color feature which brings tracking instability problems. Numerous experiments demonstrate an effective solution because the target deformation, illumination change, the target’s background color distribution similar difficulties arising track to achieve the robust target tracking algorithm
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