1,433 research outputs found
Distractor-aware deep regression for visual tracking
In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination
Visual tracking can be easily disturbed by similar surrounding objects. Such
objects as hard distractors, even though being the minority among negative
samples, increase the risk of target drift and model corruption, which deserve
additional attention in online tracking and model update. To enhance the
tracking robustness, in this paper, we propose a cascaded regression tracker
with two sequential stages. In the first stage, we filter out abundant
easily-identified negative candidates via an efficient convolutional
regression. In the second stage, a discrete sampling based ridge regression is
designed to double-check the remaining ambiguous hard samples, which serves as
an alternative of fully-connected layers and benefits from the closed-form
solver for efficient learning. Extensive experiments are conducted on 11
challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019,
UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed
method achieves state-of-the-art performance on prevalent benchmarks, while
running in a real-time speed.Comment: Accepted by IEEE TCSV
- …