In order to solve the problem of object drift caused by Kernelized Correlation Filter (KCF) tracking algorithm when scale changes, a Fast Scale Adaptive tracking of Correlation Filter (FSACF) was proposed. Firstly, a global gradient combination feature map based on salient color features was obtained by directly extracting features for the original frame image, reducing the effect of subsequent scale calculation on the performance. Secondly, the method of separating window was performed on the global feature map, adaptively selecting the scale and calculating the corresponding maximum response value. Finally, a defined confidence function was used to adaptively update the iterative template function, improving robustness of the model. Experimental result on video sets with different interference attributes show that compared with KCF algorithm, the accuracy of the FSACF algorithm by was improved 7.4 percentage points, and the success rate was increased by 12.8 percentage points; compared with the algorithm without global feature and separating window, the Frames Per Second was improved by 1.5 times. The experimental results show that the FSACF algorithm avoids the object drift when facing scale change with certain efficiency, and is superior to the comparison algorithms in accuracy and success rate.

Aiming at the phenomenon that image mosaic, to a certain extent, has uneven chromatic aberration, distortion and low efficiency, an adaptive elimination of image stitching seam and panorama alignment based fast image mosaic algorithm was proposed. Firstly, the Scale-Invariant Feature Transform (SIFT) was used to extract feature points of the specified area of the image and image registration was performed by using bidirectional K-Nearest Neighbor (KNN) algorithm, effectively improving the algorithm efficiency. Secondly, focusing on the uneven chromatic aberration transition of stitching seam, an adaptive formula for finding the optimal stitching seam was proposed based on dynamic programming, and then the seam was adaptively eliminated by image fusion. Finally, for the phenomenon of panoramic tilt caused by accumlated stitching error, an adaptive fitting quadrilateral alignment model based on edge detection algorithm was proposed to make the original panorama into a completely new panorama. Compared with the image mosaic algorithm based on block and the image mosaic algorithm based on binary tree, the proposed algorithm has the image quality improved by 5.84%-7.83% and the stitching time shortened to only 50%-70% of the original. Experimental results show that the proposed algorithm not only reduces the unevenness of chromatic aberration transition of stitching seam in different image backgrounds through adaptive update mechanism, so as to improve the image quality, but also increases the stitching efficiency and reduces the distortion degree of panorama.