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.

Hyperspectral video target tracking is generally challenging when the scale of the target varies. In this paper, a novel algorithm is proposed to address the challenges prevalent in the existing hyperspectral video target tracking approaches. The proposed approach employs deep features along with spectral matching reduction and adaptive-scale 3D hog features to track the objects even when the scale is varying. Spectral matching reduction is adopted to estimate the spectral curve of the selected target region using a weighted combination of the global and local spectral curves. In addition to the deep features, adaptive-scale 3D hog features are extracted using cube-level features at three different scales. The four weak response maps thus obtained are then combined using adaptive weights to yield a strong response map. Finally, the region proposal module is utilized to estimate the target box. The proposed strategies make the approach robust against scale variations of the target. A comparative study on different hyperspectral video sequences illustrate the superior performance of the proposed algorithm as compared to the state-of-the-art approaches.

The advent of hyperspectral cameras has popularized the study of hyperspectral video trackers. Although hyperspectral images can better distinguish the targets compared to their RGB counterparts, the occlusion and rotation of the target affect the effectiveness of the target. For instance, occlusion obscures the target, reducing the tracking accuracy and even causing tracking failure. In this regard, this paper proposes a novel hyperspectral video tracker where the double Siamese network (D-Siam) forms the basis of the framework. Moreover, AlexNet serves as the backbone of D-Siam. The current study also adopts a novel spectral–deviation-based dimensionality reduction approach on the learned features to match the input requirements of the AlexNet. It should be noted that the proposed dimensionality reduction method increases the distinction between the target and background. The two response maps, namely the initial response map and the adjacent response map, obtained using the D-Siam network, were fused using an adaptive weight estimation strategy. Finally, a confidence judgment module is proposed to regulate the update for the whole framework. A comparative analysis of the proposed approach with state-of-the-art trackers and an extensive ablation study were conducted on a publicly available benchmark hyperspectral dataset. The results show that the proposed tracker outperforms the existing state-of-the-art approaches against most of the challenges.

Object tracking using Hyperspectral Images (HSIs) obtains satisfactory result in distinguishing objects with similar colors. Yet, the tracking algorithm tends to fail when the target undergoes deformation. In this paper, a SiamRPN based hyperspectral tracker is proposed to deal with this problem. Firstly, a band selection method based on a genetic optimization method is designed for rapidly reducing the redundancy of information in HSIs. Specifically, three bands with highest joint entropy are selected. To solve the problem that the information of the template in the SiamRPN model decays over time, an update network is trained on the dataset from general objective tracking benchmark, which can obtain effective cumulative templates. The use of cumulative templates with spectral information makes it easier to track the deformed target. In addition, transfer learning of the pre-trained SiamRPN is designed to obtain a better model for HSIs. The experimental results show that the proposed tracker can obtain good tracking results over the entire public dataset, and that it is better than the other popular trackers when the target’s deformation is qualitatively and quantitatively compared, achieving an overall success rate of 57.5% and a deformation challenge success rate of 70.8%.