Learning Spatially Regularized Correlation Filters for Visual Tracking

Abstract

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model. We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers

Tracking by Natural Language Specification

“…Many modern trackers rely on discriminative correlation filters [4,12,7]. While originally selected for the Fast Fourier Transform to compute one channel quickly, Danelljan et al [10] use multiple channels to augment the discrimination of the correlation filters.…”

Learning to Track at 100 FPS with Deep Regression Networks

“…Some researchers have also attempted to use neural networks for tracking within the traditional online training framework [26,27,34,37,35,30,39,7,24,16], showing state-of-the-art results [30,7,21]. Unfortunately, neural networks are very slow to train, and if online training is required, then the resulting tracker will be very slow at test time.…”

“…Unfortunately, neural networks are very slow to train, and if online training is required, then the resulting tracker will be very slow at test time. Such trackers range from 0.8 fps [26] to 15 fps [37], with the top performing neural-network trackers running at 1 fps on a GPU [30,7,21]. Hence, these trackers are not usable for most practical applications.…”

“…Online training of neural networks tends to be very slow, preventing real-time performance. Online-trained neural network trackers range from 0.8 fps [26] to 15 fps [37], with the top performing trackers running at 1 fps on a GPU [30,7,21]. Second, most trackers evaluate a finite number of samples and choose the highest scoring one as the tracking output [26,27,37,30,39,24,33].…”

“…Our real-time speed is due to two factors. First, most previous neural network trackers are trained online [26,27,34,37,35,30,39,7,24]; however, training neural networks is a slow process, leading to slow tracking. In contrast, our tracker is trained offline to learn a generic relationship between appearance and motion, so no online training is required.…”