# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 2 Related Work  
The tracking performance using traditional features is severely restricted when tracking scenarios are complex. Distinct from handcrafted features, the emergence of deep-learning-based approaches has provided a significant increase in performance. Tracking algorithms based on deep feature representations have achieved state-of-the-art accuracy. Although these techniques perform well on benchmarks, they often suffer from tracking drift caused by the accumulation of errors. Recently, derived from the idea of tracking by detection, trackers based on the Siamese network have received wide attention. Siamese-based trackers formulate the tracking problem as a similarity learning function and predict the object location by comparing the similarity between the template image and the search image. The Siamese networks are trained offline on large-scale image pairs. The pioneering method SiamFC [16] uses the Siamese network as a feature extractor and introduces a cross-correlation layer to generate a single channel response map. The correlation can be seen as a similarity calculation, and the response map reflects the similarity between the template and the search region. Following this similarity-learning work, [PERSON] et al. propose SiamRPN [14], which enhances the tracking performance by integrating a region proposal network (RPN) into SiamFC. The RPN has two branches: one classification branch in charge of scoring the probability, and the other, a regression branch, is responsible for estimating the coordinates of bounding boxes. Based on SiamRPN, Da-SiamRPN [17] addresses the problem of the imbalance between non-semantic negative examples and semantic distractors of training data through data augmentation. UpdateNet [13] further improves upon DaSiamRPN by incorporating a small network that learns the appearance change of tracked targets. SiamDW [18] takes advantage of deeper neural networks by eliminating the negative impact of padding. SiamRPN++ [15] further improves upon the object-tracking performance using deeper networks and multi-layer fusion, achieving better accuracy while maintaining fair speed. To eliminate the negative effects of anchors, SiamCAR [19] adopts two subnetworks for feature extraction and regression respectively and proposes an anchor-free framework; SiamBAN [20] directly classifies objects and regresses bounding boxes taking advantage of a unified fully convolutional network. The avoidance of setting pre-defined anchors can avoid the tricky hyper-parameter tuning, easing the effect of human intervention. To solve the problem that the size of the object feature region needs to be determined in advance, and the cross-correlation method either retains a lot of unfavorable background information or loses a lot of foreground information, SiamGAT [21] proposes a Graph Attention Module (GAM) to establish a partial correspondence between an object and a search region as a complete bipartite graph. In long-term tracking, robustness is a common weak point. Siam R-CNN [22] has a two-stage Siamese re-detection architecture and re-detects images by comparing region proposals with the template region. LTMU [23] proposed a meta-updater that guides the tracker update, forming a long-term tracking framework along with an online local tracker, an online verifier, and a SiamRPN-based re-detector. These methods significantly improved tracking precision but have a low tracking frame rate even in high-end desktops. [PERSON] et al. [24] presented a long-term target tracking method by combining adaptive discriminative correlation filters with a support vector machine-based component. Siam-RM [25] has an object-tracking framework that uses the Siamese network and adopts the Siamese instance search tracker as the re-detection network. [PERSON] et al. [26] deployed local-global multiple correlation filters for tracking and a Kalman filter re-detection model for re-detection when the correlation filters are unreliable.