Hyperspectral Video Target Tracking Based on Deep Edge Convolution Feature and Improved Context Filter

Abstract

To address the problem that the performance of hyperspectral target tracking will be degraded when facing background clutter, this paper proposes a novel hyperspectral target tracking algorithm based on the deep edge convolution feature (DECF) and an improved context filter (ICF). DECF is a fusion feature via deep features convolving 3D edge features, which makes targets easier to distinguish under complex backgrounds. In order to reduce background clutter interference, an ICF is proposed. The ICF selects eight neighborhoods around the target as the context areas. Then the first four areas that have a greater interference in the context areas are regarded as negative samples to train the ICF. To reduce the tracking drift caused by target deformation, an adaptive scale estimation module, named the region proposal module, is proposed for the adaptive estimation of the target box. Experimental results show that the proposed algorithm has satisfactory tracking performance against background clutter challenges.

Keywords

• hyperspectral video target tracking
• deep edge convolution feature
• improved context filter
• region proposal module