  Modern visual trackers usually construct online learning models under the
assumption that the feature response has a Gaussian distribution with
target-centered peak response. Nevertheless, such an assumption is implausible
when there is progressive interference from other targets and/or background
noise, which produce sub-peaks on the tracking response map and cause model
drift. In this paper, we propose a rectified online learning approach for
sub-peak response suppression and peak response enforcement and target at
handling progressive interference in a systematic way. Our approach, referred
to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to
aggregate and align discriminative features, as well as leveraging a Boundary
Response Truncation (BRT) to reduce the variance of feature response. By fusing
with multi-scale features, SPSTracker aggregates the response distribution of
multiple sub-peaks to a single maximum peak, which enforces the discriminative
capability of features for robust object tracking. Experiments on the OTB, NFS
and VOT2018 benchmarks demonstrate that SPSTrack outperforms the
state-of-the-art real-time trackers with significant margins.
