We propose an object tracking method, SFTrack++, that smoothly learns to
preserve the tracked object consistency over space and time dimensions by
taking a spectral clustering approach over the graph of pixels from the video,
using a fast 3D filtering formulation for finding the principal eigenvector of
this graph's adjacency matrix. To better capture complex aspects of the tracked
object, we enrich our formulation to multi-channel inputs, which permit
different points of view for the same input. The channel inputs are in our
experiments, the output of multiple tracking methods. After combining them,
instead of relying only on hidden layers representations to predict a good
tracking bounding box, we explicitly learn an intermediate, more refined one,
namely the segmentation map of the tracked object. This prevents the rough
common bounding box approach to introduce noise and distractors in the learning
process. We test our method, SFTrack++, on five tracking benchmarks: OTB, UAV,
NFS, GOT-10k, and TrackingNet, using five top trackers as input. Our
experimental results validate the pre-registered hypothesis. We obtain
consistent and robust results, competitive on the three traditional benchmarks
(OTB, UAV, NFS) and significantly on top of others (by over $1.1\%$ on
accuracy) on GOT-10k and TrackingNet, which are newer, larger, and more varied
datasets.

本文提出了一种基于谱聚类的目标追踪方法 SFTrack++，其中通过多通道输入和分割映射的方式来提高算法的追踪效果，同时在 OTB、UAV、NFS、GOT-10k 和 TrackingNet 等多个数据集上进行了实验验证，结果表明该方法具有较好的追踪精度。