Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. How
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Abstract. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1 % in mean OP), Temple-Color (+4.6 % in mean OP), and VOT2015 (20 % relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.
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Introduction
Visual tracking is the task of estimating the trajectory of a target in a video. It is one of the fundamental problems in computer vision. Tracking of objects or feature points has numerous applications in robotics, structure-from-motion, and visual surveillance. In recent years, Discriminative Correlation Filter (DCF) based approaches have shown outstanding results on object tracking benchmarks [30,46]. DCF methods train a correlation filter for the task of predicting the target classification scores. Unlike other methods, the DCF efficiently utilize all spatial shifts of the training samples by exploiting the discrete Fourier transform. Deep convolutional neural networks (CNNs) have shown impressive performance for many tasks, and are therefore of interest for DCF-based tracking. A CNN consists of several layers of convolution, normalization and pooling operations. Recently, activations from the last convolutional layers have been successfully employed for image classification. Features from these deep convolutional Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46454-1 29) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part V, LNCS 9909, pp. 472–488, 2016. DOI: 10.1007/978-3-319-46454-1 29
Learning Continuous Convolution Operators for Visual Tracking
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Fig. 1. Visualization of our continuous convolution operator, applied to a multiresolution deep feature map. The feature map (left) consists of the input RGB patch along with the first and last convolutional layer of a pre-trained deep network. The second column visualizes the continuous convolution filters learned by our framework. The resulting continuous convolution outputs for each layer (third column) are combined into the final continuous confidence function (right) of the targ
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