Robust Visual Tracking Based on Multi-channel Compressive Features

Abstract

Tracking-by-detection approaches show good performance in visual tracking, which often train discriminative classifiers to separate tracking target from their surrounding background. As we know, an effective and efficient image feature plays an important role for realizing an outstanding tracker. The excellent image feature can separate the tracking object and the background more easily. Besides, the feature should effectively adapt to many boring factors such as illumination changes, appearance changes, shape variations, and partial or full occlusions, etc. To this end, in this paper, we present a novel multi-channel compressive feature, which combine rich information from multiple channels, and then project it into a low-dimension compressive feature space. After that, we designed a new visual tracker based on the multi-channel compressive features. At last, extensive comparative experiments conducted on a series of challenging sequences demonstrate that our tracker outperforms most of state-of-the-art tracking approaches, which also proves that our multi-channel compressive feature is effective and efficient.