In outlier detection problem, most existing algorithms have a notable issue that these approaches cannot detect highdimension outliers effectively. In order to provide a practical solution for this problem, we propose an outlier detection algorithm based on robust component analysis. The basic idea is to train multiple base detectors with the robust component analysis results of the training dataset. Furthermore, we generate some virtual outliers and utilize them to test the capacities of based detectors, and combine them according to the test results to obtain the final outlier detector. Experimental results comparing the proposed method with baseline approaches are presented on several datasets showing the performance of our approach.
Focusing on the issue that Correlation Filter Trackers has poor performance in scale variations, a fine scale estimation approach is proposed. Firstly, we train a scale correlation filter using the target initial state. Secondly, the target is segmented according to its shape and then two subgraph correlation filters are respectively established. During tracking, we judge the trend of scale changes by the relative position changes of the subgraphs and the weights of the scale samples are offset. In this way, we obtain the coarse scale estimation of the target. Finally, we use Newton method to accurately estimate the scale of the target. Experiments show that the algorithm achieves more accurate scale estimation and effectively improves the tracking success rate.
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