Object tracking with adaptive elastic net regression
Shunli Zhang, Weiwei Xing · 2017
Recently, various regression based tracking methods have achieved great success. However, in most of these methods, all of the extracted features are made use of to represent the object without feature selection. In this paper, we propose a novel tracking method based on elastic net regression with adaptive weights. On one hand, tracking is formulated as an elastic net regression problem which can not only make full use of the spatial information, but also automatically select features to alleviate the influence of the unstable or inaccurate points. On the other hand, the weights of the ℓ1-norm and ℓ2-norm regularization in the regression model are adaptively adjusted to better improve the performance. Experimental results in the benchmark dataset demonstrate that the proposed adaptive elastic net regression based tracking method can achieve desirable tracking performance.