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Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

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In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers.

Camera calibration is one of the essential techniques in the 3D computer vision field. Through calibration, the distortion problem can be solved with registration and by acquiring the intrinsic and extrinsic parameters of the camera. In this study, we use and develop more realistic contents through color information and image quality that is obtained by projecting multi-view images without distortion. These images are projected from a multi-projector to a screen. The proposed method solves the distortion problem that occurs when projecting an image to a screen using multi-projector projection. The no distortion reference image is built and projected by a multi-projector onto a screen with registration of the distortion target image and reference image by estimating the homography that shows the relationship between the reference image and distorted target image. In the experimental results, the images projected by the multi-projector are better than those projected by a single projector with respect to resolution and color. Further, the test environment consists of server/client and it can be projected without any distortion to a screen, regardless of the number of projectors.

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Visual object tracking has many applications related to computer vision. Recently, correlation filter based trackers have been ranked as the highest performers in this field. However, handling chronic problems such as occlusion, deformation, and scale variations is difficult with such trackers. These problems are solved by many other researches that employ other features and improve an appearance update. In this paper, we propose an improved CSK (Circulant Structure with Kernel) tracker using object feature decomposition in the wavelet domain. Specifically, a newly created correlation kernel is generated from different filter-banks reflecting the visual properties of a given object, and it is stable and robust to the environmental variations. Experimental results demonstrate that the proposed scheme outperforms the conventional CSK tracker in terms of center location error by 59% on an average for 100 sequences. Therefore, we believe that the proposed tracker can be useful for robust object tracking in occlusion.