Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

Abstract

Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made it a challenging task for unmanned aerial vehicles (UAV) to perform robust and accurate object following. Traditional hand-crafted features are also not precise and robust enough to describe the object in the viewing point of UAV. In this work, a novel tracker with online enhanced background learning is specifically proposed to tackle boundary effects. Real background samples are densely extracted to learn as well as update correlation filters. Spatial penalization is introduced to offset the noise introduced by exceedingly more background information so that a more accurate appearance model can be established. Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object. In order to mitigate changes of objects' appearances, multi-frame technique is applied to learn an ideal response map and verify the generated one in each frame. Exhaustive experiments were conducted on 100 challenging UAV image sequences and the proposed tracker has achieved state-of-the-art performance.

AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization

“…b) Filter degradation: the appearance model updated via a linear interpolation method cannot adapt to ubiquitous appearance change, leading to filter degradation. Some attempts are made to tackle the issue, e.g., training set management [20,24,32], temporal restriction [14,25], tracking confidence verification [23,12] and over-fitting alleviation [33]. Amongst them the temporal regularization is an effective and efficient way.…”

“…To improve DCF-based trackers, there are currently three directions: a) building more robust appearance model [18,17,20,21], b) mitigating boundary effect or imposing restrictions in learning [8,22,17,14,23], and c) mitigating filter degradation [24,12,14,25]. Robust appearance can indeed boost performance, yet it leads to burdensome calculations.…”

Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking

“…Context and background information are also exploited to have more negative samples so that learned correlation filters can have more discriminative power [7,15,21]. Besides hand-crafted features used in [7,13,15,18], the application of deep features is also investigated for more precise and comprehensive object appearance representation [6,12,19]. Some trackers combine hand-crafted features with deep ones to better describe the tracked objects from multiple aspects [5,16].…”

“…As was stated before, traditional DCF based framework usually suffers from boundary effects due to the limited search region originating from its periodic shifting of the area near original object. Some measures are already taken to mitigate this effect [7,12,15]. Spatially regularized DCF (SRDCF) was proposed to introduce punishment for background in training correlation filters so that they can be learned in larger search regions [7].…”

“…Backgroundaware correlation filter (BACF) extracts patches densely from background using cropping matrix [15], which expands search region with lower computational cost. Background effect-aware visual tracker (BEVT) merges these two methods, thus achieving a better performance [12].…”