Although various visual tracking algorithms have been proposed in the last 2–3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the “curse of dimensionality” has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.
In recent years, many studies consider visual tracking as a two-class classification problem. The key problem is to construct a classifier with sufficient accuracy in distinguishing the target from its background and sufficient generalize ability in handling new frames. However, the variable tracking conditions challenges the existing methods. The difficulty mainly comes from the confused boundary between the foreground and background. This paper handles this difficulty by generalizing the classifier’s learning step. By introducing the distribution data of samples, the classifier learns more essential characteristics in discriminating the two classes. Specifically, the samples are represented in a multiscale visual model. For features with different scales, several large margin distribution machine (LDMs) with adaptive kernels are combined in a Baysian way as a strong classifier. Where, in order to improve the accuracy and generalization ability, not only the margin distance but also the sample distribution is optimized in the learning step. Comprehensive experiments are performed on several challenging video sequences, through parameter analysis and field comparison, the proposed LDM combined ensemble tracker is demonstrated to perform with sufficient accuracy and generalize ability in handling various typical tracking difficulties.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.