# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 2 Related Work
### Generic Visual Trackers  
[PERSON] [19] combined sparsity-based discriminative classifiers with sparsity-based generative models to cover appearance changes in target objects. [PERSON] [20] presented a visual tracking method based on a structured output support vector machine, which enables to couple label prediction with object position estimation. TLD [21] transformed visual tracking problems into a combination of tracking, learning, and detection problems. Tracking aims to associate similar detection results across consecutive frames, whereas detection determines target areas. Learning updates detectors to reduce errors. ASLA [22] represented target appearances using a novel alignment-pooling method, in which partial and spatial structures of target appearances are exploited to deal with occlusion and geometric deformation. CXT [23] exploited distracters and supporters using sequential randomized forests. Distracters make visual trackers to drift into background regions, thus they should be avoided during visual tracking. Supporters help visual trackers to find foreground regions, thus they can improve visual tracking accuracy. [PERSON] [24] sampled multiple visual trackers using Markov Chain Monte Carlo methods by proposing different appearance, motion, state, and observation models, in which these trackers run interactively to track target objects in real-world scenarios. VTD [25] decomposed conventional visual tracking models into different combinations of basic appearance and motion models, in which each combination deals with a particular appearance and motion changes in target objects. CSK [26] employed circulant matrices for visual tracking, which enables fast learning and detection. LSK [27] selected representative bases from a static sparse dictionary for visual tracking, which is updated adaptively over time. LOT [28] proposed a visual tracking method based on a probabilistic model to handle different variations especially in deformable objects. SiamDW [29] used deeper and wider neural networks for visual tracking, in which receptive fields and network strides are adaptively controlled via residual modules. SiamRPN++ [30] adopted ResNet architectures with layer-wise and depth-wise feature aggravation for multi-level feature fusion. SINTop [31] required no appearance model updating for visual tracking and simply found the best patch, which is mostly similar to the initial target patch, using a trained matching function. DAT [32] presented attention-based visual trackers using a reciprocative learning algorithm, which focus more on temporally representative features. TADT [33] introduced regression and ranking losses with Siamese networks to generate target-aware and scale-sensitive features for visual tracking. ECO-HC [34] reduced the number of network parameters and computational complexity in visual tracking by utilizing factorized convolution operators and compact generative models. COT [35] developed continuous convolution filters defined in in a continuous spatial domain, which can fuse multi-level deep features. In contrast, our method uses a simple object proposal voting scheme without complex deep neural architectures for visual tracking.