# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 2 Related Work  
Existing object tracking algorithms mainly consist of two categories: generative model methods and discriminative model methods [2,12,13]. The generative model method builds a model for the given target area in the initial frame. The target location is estimated via searching for the area which is most similar to the model in the subsequent frames. Typical algorithms of the generative model method are adaptive structural local sparse appearance (ASLA) [14], locality sensitive histograms (LSHT) [15], locally orderless tracking (LOT) [16], and scale-adaptive mean-shift (SAMS) [17]. However, these tracking algorithms usually lack the discriminative capability of distinguishing the target from its background. Consequently, the generative model-based algorithms tend to suffer low tracking accuracy, thus limiting their applications. Most state-of-the-art tracking algorithms adopt the tracking-by-detection method, which is a form of discriminative model. A discriminative model method [18, 19, 20, 21, 22, 23, 24, 25, 26, 27] treats object tracking as an object detection task in each frame wherein image features are collected to train a classifier. Specifically, the target area and background are regarded as positive samples and negative samples, respectively. The optimal solution in the subsequent frames can be acquired using the trained classifier. In the tracking process, the output in each frame is continuously used as a positive sample and the background as a negative sample for training the classifier online. Recently, correlation filters [18, 19, 20, 21, 22, 23, 24, 25, 26, 27] and deep learning-based trackers [28, 29, 30, 31, 32, 33, 34, 35] have become two active research topics of discriminative model methods with outstanding performance. Owing to the attractive property of extracting deep features, trackers based on deep learning have demonstrated great potential for the object tracking task. [PERSON] et al. proposed a fully-convolution Siamese network (SiamFC) [29] for object tracking, which extracted deep features of the searching and target areas using the same fully-convolutional network to find the target position. [PERSON] et al. proposed an asymmetric Siamese network architecture named CFNet [30]. They interpreted the correlation filter learner as a differentiable layer in a deep neural network and made full use of the advantages of the correlation filter in object tracking. [PERSON] et al. proposed the Attentional Correlation FilterNetwork (ACFN) [31], which introduced an attentional mechanism into a novel tracking framework to increase robustness and computational efficiency. [PERSON] and [PERSON] proposed a multi-domain convolutional neural network (CNN) for visual tracking, which was referred to as a Multi-Domain Network (MDNet) [32]. It pre-trained a CNN using a large set of data to obtain a generic target representation. Subsequently, the domain-specific layers of the CNN were trained online to learn the specific target representation of the tracking target in a new sequence. Recently, [PERSON] et al. [33] proposed an anti-occlusion tracker which extracted features from different layers of a residual neural network (ResNet) to produce response maps and an occlusion detection strategy was introduced to prevent model drift. [PERSON] et al. [34] exploited a novel deconvolution network to enlarge the low spatial resolution feature maps in a deep convolution network and summarized the feature maps to better represent the target appearance. Generally, deep learning-based algorithms rely on expensive hardware components such as graphics processing units (GPUs). In addition, a large number of datasets are required to pre-train the network. In spite of great performance in terms of accuracy, their running speed is generally slow on central processing unit (CPUs).