This paper presents a new stereo based on effective cost aggregation. Firstly, a color-based image segmentation algorithm is used to segment the reference image into blocks with different colors, and they are taken as the matching window to search the similar region in the target image. Secondly, in order to eliminate the mismatching of brightness difference and noise, Census transform is conducted with both reference image and target image to obtain the bit strings. At the same time, for the sake of matching efficiency, it is of great necessary to rectify the dynamic disparity scopes of the pixel to be matched by the disparity of neighborhood pixels. At last, the bit strings can be used to execute the cost calculation by a new kind of cost aggregation function. By moving the window in the horizontal direction, we can figure out the percentage of the mismatch pixels in every segmented block respectively. The minimum value means which corresponding the horizontal shift is expressed as the best disparity of this segmented block. The experimental results demonstrate that our method can not only improve the accuracy of the depth discontinuities, but also has the high calculation efficiency. Besides, it has good robustness in different brightness environments.
Tracking methods based on Correlation Filter have been constantly improved in tracking accuracy and robustness. However, it still challenged in background clutter, rotation changes and occlusion, the drift of the model was one of the main reasons. In this paper, we propose an online sample training method based on Gaussian Mixture Model. The maximum response value, obtained from the convolution of samples and filters, is used to judge the availability of the online samples, which is able to reduce the interference of wrong online samples. Then, through Gaussian Mixture Model, samples are classified to strengthen the diversity of the sample set, which can avoid model drift effectively. Besides, we also propose a model update criterion to enhance the stability of the tracker, and heighten the efficiency of calculation. This criterion is determined by changes of target in scale and displacement. We perform comprehensive experiments on three benchmarks: OTB100, VOT2016 and VOT-TIR2016. Comparing with other trackers, our tracker has better robustness in the condition of background clutter, rotation change and occlusion. Moreover, its speed also maintains real-time performance.
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