Visual object tracking via a manifold regularized discriminative dual dictionary model. (July 2019)

• Record Type: Journal Article
• Authors: Wang, Lingfeng; Pan, Chunhong
• Is Part Of: Pattern recognition. Volume 91(2019:Jul.)
• Page Start: 272
• Page End: 280
• Publication Date: 2019-07
• Subjects: Visual tracking -- Dictionary learning -- Dual dictionary -- Manifold regularization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.02.008
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a Manifold Regularized Discriminative Dual Dictionary (MD 3 ) model. MD 3 model introduces both spatial and temporal information, which are represented by discriminative dictionary and manifold regularization, respectively. An alternating optimization method is introduced to solve MD 3 model. Particle Filtering tracking framework is utilized to incorporate MD 3 model, in which observation model is represented by reconstruction error. Abstract: Real-time object tracking plays an important role in many computer vision systems, yet in complex scenarios, it is still a very challenging problem. In this paper, we propose a new visual tracking algorithm via a manifold regularized discriminative dual dictionary (MD 3 ) model. First, a dual dictionary is introduced to avoid the calculation of representation coefficient in distance function construction. Second, the local background templates are utilized to keep the learned dictionaries discriminative. Third, the manifold regularization on representation coefficient is proposed to ensure that MD 3 model has a bit error tolerance on the object update. We formulate object tracking in a particle filter framework, in which the observation model is calculated as the reconstruction error between learned dictionaries and the candidate template. Extensive experiments in various tracking scenarios are performed to evaluate the proposed method, and the results interpret that the tracking accuracy as well as the computational cost can be improved as compared with the state-of-the-art approaches.