Siamese networks are prevalent in visual tracking because of the efficient localization. The networks take both a search patch and a target template as inputs where the target template is usually from the initial frame. Meanwhile, Siamese trackers do not update network parameters online for real-time efficiency. The fixed target template and CNN parameters make Siamese trackers not effective to capture target appearance variations. In this paper, we propose a template updating method via reinforcement learning for Siamese regression trackers. We collect a series of templates and learn to maintain them based on an actor-critic framework. Among this framework, the actor network that is trained by deep reinforcement learning effectively updates the templates based on the tracking result on each frame. Besides the target template, we update the Siamese regression tracker online to adapt to target appearance variations. The experimental results on the standard benchmarks show the effectiveness of both template and network updating. The proposed tracker SiamRTU performs favorably against state-of-the-art approaches.

Visual tracking is one of the fundamental tasks in computer vision with many challenges, and it is mainly due to the changes in the target's appearance in temporal and spatial domains. Recently, numerous trackers model the appearance of the targets in the spatial domain well by utilizing deep convolutional features. However, most of these CNN-based trackers only take the appearance variations between two consecutive frames in a video sequence into consideration. Besides, some trackers model the appearance of the targets in the long term by applying RNN, but the decay of the target's features degrades the tracking performance. In this article, we propose the antidecay long short-term memory (AD-LSTM) for the Siamese tracking. Especially, we extend the architecture of the standard LSTM in two aspects for the visual tracking task. First, we replace all of the fully connected layers with convolutional layers to extract the features with spatial structure. Second, we improve the architecture of the cell unit. In this way, the information of the target appearance can flow through the AD-LSTM without decay as long as possible in the temporal domain. Meanwhile, since there is no ground truth for the feature maps generated by the AD-LSTM, we propose an adversarial learning algorithm to optimize the AD-LSTM. With the help of adversarial learning, the Siamese network can generate the response maps more accurately, and the AD-LSTM can generate the feature maps of the target more robustly. The experimental results show that our tracker performs favorably against the state-of-the-art trackers on six challenging benchmarks: OTB-100, TC-128, VOT2016, VOT2017, GOT-10k, and TrackingNet.

Recently, deep Siamese matching networks have attracted increasing attention for visual tracking. Despite the demonstrated successes, Siamese trackers do not take full advantage of the structural information of target objects. They tend to drift in the presence of non-rigid deformation or partly occlusion. In this paper, we propose to advance Siamese trackers with graph convolutional networks, which pay more attention to the structural layout of target objects, to learn features robust to large appearance changes over time. Specifically, we divide the target object into several sub-parts and design an attentive graph convolutional network to model the relationship between parts. We incrementally update the attention coefficients of the graph with the attention scheme at each frame in an end-to-end manner. To further improve localization accuracy, we propose a learnable cascade regression algorithm based on deep reinforcement learning to refine the predicted bounding boxes. Extensive experiments on seven challenging benchmark datasets, i.e., OTB-100, TC-128, VOT2018, VOT2019, TrackingNet, GOT-10k and LaSOT, demonstrate that the proposed tracking method performs favorably against state-of-the-art approaches.

A number of visual tracking methods achieve the state-of-the-art performance based on deep learning recently. However, most of these trackers utilize the deep neural network in regression task or classification task separately. In this paper, we propose an adversarial deep tracking framework. The framework is composed of a fully convolutional Siamese neural network (regression network) and a discriminative classification network. Then, we jointly optimize the regression network and the classification network by adversarial learning. In the uniform framework, the regression network and classification network can be trained end-to-end as a whole using large amounts of video training data sets. During the testing phase, the regression network generates a response map which reflects the location and the size of the target within each candidate search patch, and the classification network discriminates which response map is the best in terms of the corresponding template patch and candidate search patch. In addition, we propose an attention visualization algorithm for our tracker, and it reflects the area that attracts the attention of our tracker during tracking. The experimental results on three large-scale visual tracking benchmarks (OTB-100, TC-128, and VOT2016) demonstrate the effectiveness of the proposed tracking algorithm and show that our tracker performs comparably against the state-of-the-art trackers.

How to track an arbitrary object in video is one of the main challenges in computer vision, and it has been studied for decades. Based on hand-crafted features, traditional trackers show poor discriminability for complex changes of object appearance. Recently, some trackers based on convolutional neural network (CNN) have shown some promising results by exploiting the rich convolutional features. In this paper, we propose a novel DenseTracker based on a mutli-task dense convolutional network. To learn a more compact and discriminative representation, we adopt a dense block structure to ensemble features from different layers. Then a multitask loss is designed to accurately predict the object position and scale by joint learning of box regression and pair-wise similarity. Furtherly, the DenseTracker is trained end-to-end on large-scale datasets including ImageNet Video (VID) and ALOV300++. The DenseTracker runs in 25 fps on GPU and achieves the state-of-the-art performance on two public benchmarks of OTB50 and VOT2016.