Abstract

It is a challenging task to develop effective and efficient ap- pearance models for robust ob ject tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing on- line tracking algorithms often update models with samples from obser- vations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a re- sult of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs non- adaptive random pro jections that preserve the structure of the image feature space of ob jects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We com- press samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the com- pressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.