Subspace representation model is an important subset of visual tracking algorithms. Compared with models performed on the original data space, subspace representation model can effectively reduce the computational complexity, and filter out high dimensional noises. However, for some complicated situations, e.g., dramatic illumination changing, large area of occlusion and abrupt object drifting, traditional subspace representation models may fail to handle the visual tracking task. In this paper, we propose a novel subspace representation algorithm for robust visual tracking by using low-rank representation with graph constraints (LRGC). Low-rank representation has been well-known for its superiority of handling corrupted samples, and graph constraint is flexible to characterize sample relationship. In this paper, we aim to exploit benefits from both low-rank representation and graph constraint, and deploy it to handle challenging visual tracking problems. Specifically, we first propose a novel graph structure to characterize the relationship of target object in different observation states. Then we learn a subspace by jointly optimizing low-rank representation and graph embedding in a unified framework. Finally, the learned subspace is embedded into a Bayesian inference framework by using the dynamical model and the observation model. Experiments on several video benchmarks demonstrate that our algorithm performs better than traditional ones, especially in dynamically changing and drifting situations.

For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.

SQUID (Superconducting QUantum Interference Device) Photoscanning is an analytical technique intended for the noninvasive evaluation of semiconductor wafers and device structures. This method is based on the detection of the magnetic field of photocurrents locally induced in the sample under investigation by a focused laser beam. The magnetic field is monitored by means of a sensitive SQUID magnetometer while scanning the sample surface with the laser beam. Doping inhomogeneities in electronic grade silicon, grain boundaries in solar silicon, and defects in photovoltaic device structures have been analyzed.