Deep Regression Tracking with Shrinkage Loss
Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions. Due to the potential for fast tracking and easy implementation, regression trackers have recently received increasing attention. However, state of the art deep regression trackers do not perform as well as discriminative correlation filters (DCFs) trackers. We identify the main bottleneck of training regression networks as extreme foreground background data imbalance. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data. Additionally, we apply residual connections to fuse multiple convolutional layers as well as their output response maps. Without bells and whistles, the proposed deep regression tracking method performs favorably against state of the art trackers, especially in comparison with DCFs trackers, on five benchmark datasets including OTB 2013, OTB 2015, Temple 128, UAV 123 and VOT 2016.
ECCV