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Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms. [ABSTRACT FROM AUTHOR]

As a new method of Earth observation, video satellite can provide high-temporal resolution remote sensing images for object tracking. Object tracking in satellite videos is promising yet challenging in computer vision. Although many algorithms for satellite video object tracking have been proposed, none of them solve the problem of tracking rotating object. Due to the nadir view, the rotation of an object is very common in the satellite videos. This problem urgently needs to be addressed. In this paper, a rotation-adaptive correlation filter (RACF) tracking algorithm is proposed to address the problem caused by the rotation of object. The proposed algorithm provides the following improvements: (a) A method of estimating the object rotation angle to keep the feature map stable during the object rotation is proposed. This method can overcome the drawback of histogram of oriented gradient (HOG) based trackers, which cannot deal with the rotation of objects in satellite videos; and (b) making the algorithm capable of estimating the change in the bounding box size caused by object's rotation. The experimental results demonstrate that our algorithm can track object with a 99.84% precision score and 92.96% success score in six videos from the Jilin-1 satellite constellation. [ABSTRACT FROM AUTHOR]

In this paper, a novel YOLO-based detection model with deformable convolution network (DCN-YOLOv5) is developed, which is concerned with the object and key points detection and behavior tracking problem for Oplegnathus punctatus in the ammonia nitrogen environment. The proposed model can adapt to the posture change of the object by deforming the receptive field, which solves the problem of false and missed detection caused by the movement and occlusion. Moreover, a new multi-object multi-category tracking algorithm (MOMC-Tracking) is proposed to track and plot the trajectory and calculate the key behavioral characteristics parameters. In addition, an executable software which integrates the proposed DCN-YOLOv5 model and the MOMC-Tracking algorithm is proposed. Extensive experiments show that compared with the typical YOLO series of algorithms, the proposed model in this paper performs the best with the highest accuracy and the fastest convergence speed, where the mAP @0.5 and mAP @0.5:0.95 of the proposed DCN-YOLOv5 model are 93.71% and 57.45%, which are respectively improved by 1.78% and 24.77% as compared with those obtained by the original YOLOv5 model. [ABSTRACT FROM AUTHOR]

Correlation Filter (CF) based algorithms play an important role in the field of Visual Object Tracking (VOT) due to their high accuracy and low computational complexity. While existing CF tracking algorithms suffer performance degradation due to inaccurate object modeling. In this paper, we improve the object modeling accuracy in both CF training stage and target detection procedure to preventing the drift problem. Specifically, we propose a multi-model structure for CF trackers to capture the target appearance changes, where different appearance models are trained with specific samples to catch the salient features of the target and reduce the computational cost. Furthermore, a space filter for detection features is designed to suppress the boundary effect under Gaussian motion prior, which contributes to improving the accuracy of position estimation. We deploy our method to three hand-crafted features based CF trackers to perform real-time visual tracking on popular benchmarks. The experimental results demonstrate the efficacy of our proposed scheme and the efficiency of our trackers. In addition, we provide a comprehensive analysis of the proposed method to facilitate application. [ABSTRACT FROM AUTHOR]

With the development of deep learning, the performance of many computer vision tasks has been greatly improved. For visual tracking, deep learning methods mainly focus on extracting better features or designing end-to-end trackers. However, during tracking specific targets most of the existing trackers based on deep learning are less discriminative and time-consuming. In this paper, a cascade based tracking algorithm is proposed to promote the robustness of the tracker and reduce time consumption. First, we propose a novel deep network for feature extraction. Since some pruning strategies are applied, the speed of the feature extraction stage can be more than 50 frames per second. Then, a cascade tracker named DCCT is presented to improve the performance and enhance the robustness by utilizing both texture and semantic features. Similar to the cascade classifier, the proposed DCCT tracker consists of several weaker trackers. Each weak tracker rejects some false candidates of the tracked object, and the final tracking results are obtained by synthesizing these weak trackers. Intensive experiments are conducted in some public datasets and the results have demonstrated the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Visual object tracking is a challenging and fundamental research topic in the field of computer vision. Recent years, many subspace-learning based methods have been proposed for visual object tracking with promising results. These methods reconstruct candidate states by a set of basis vectors and select the best state with minimum reconstruction error. It is well known that the accuracy of reconstructed results is seriously affected by partial occlusion. Besides, updating with occlusions is likely to mislead the tracker to drift away. Existing methods either do not consider these situations or find occlusion regions only by current observation and reconstruction. In fact, occlusion regions usually come from the background regions in previous frames, which are neglected in existing methods. Under this assumption, a novel object tracking algorithm called Partial Occlusion by Background Alignment (POBA) is proposed, which aims to find the best candidate state with an accurate occlusion mask. The POBA tracker treats current observation as a combination of object appearance and occlusion regions. The object appearance is modelled by basis vectors through incremental PCA over grayscale images. Then the occlusion region is reconstructed from last frame under the assumption that the backgrounds between two consecutive frames are almost identical. Besides, most candidate states are different from the object obviously, which can be filtered by some predefined occlusion masks so that computational complexity can be further reduced. Finally, the POBA tracker was analyzed on 8 challenge sequences and evaluated on two challenging datasets including OTB2015 and Temple Color. It gets an AUC of 0.456, a success rate score of 0.538 and a precision score of 0.626 in OPE on OTB2015 dataset. All indicators are increased by more than 23% compared with the 6 classical trackers. [ABSTRACT FROM AUTHOR]

• A novel AS layer is proposed to improve the expression power of Siamese network. • Auxiliary relocation branch to refine tracking results and introduce prior knowledge. • Switch function to monitor tracking process and evaluate the status of AS-SiamFC. • Impact of pooling layer on Siamese network is discussed in the experiment part. Siamese network is highly regarded in the visual object tracking filed because of its unique advantages of pairwise input and pairwise training. It can measure the similarity between two image patches, which coincides with the principle of the matching-based tracking algorithm. In this paper, a variant Siamese network based tracker is proposed to introduce attention module into traditional Siamese network, and relocate the object with some auxiliary relocation methods, when the proposed tracker runs under an untrusted state. Firstly, a novel attention shake layer is proposed to replace the max pooling layer in Siamese network. This layer could introduce and train two different kinds of attention modules at the same time, which means the proposed attention shake layer could also help to improve the expression power of Siamese network without increasing the depth of the network. Secondly, an auxiliary relocation branch is proposed to assist in object relocation and tracking. According to the prior assumptions of visual object tracking, some weights are involved in the auxiliary relocation branch, such as structure similarity weight, motion similarity weight, motion smoothness weight and object saliency weight. Thirdly, a novel response map based switch function is proposed to monitor the tracking process and control the effect of auxiliary relocation branch. Furthermore, in order to discuss the effect of pooling layer in Siamese network, 9 pooling and attention architectures are proposed and discussed in this paper. Some empirical results are shown in the experiment part. Comparing with the state-of-the-art trackers, the proposed tracker could achieve comparable performance in multiple benchmarks. [ABSTRACT FROM AUTHOR]

• Multi samples are combined into a sample pool to model various object appearances. • A dynamic selection strategy is proposed to manage the sample components. • Multi-feature fusion strategy is presented according to feature response maps. One of the main factors that limit the accuracy and robustness of visual tracking algorithms is the lack of suitable appearance models. The robustness and effectiveness of object appearance models is severely affected by the changing object appearances during the tracking process and the interference of other similar objects around the truth object. In this paper, a self-adaptive appearance model pool based on multi-sample is constructed to improve the robustness of the object appearance models. In order to deal with variable object states, the initial sample given in the first frame and the samples generated in the subsequent tracking process are combined into a sample set to represent various appearances of the object. In addition, a dynamic selection strategy is explored to update and maintain the sample components that are derived from varieties of sources. In order to distinguish the tracking object from other similar candidate objects, multi-feature response fusion strategy is proposed, which can effectively improve the expression ability of the appearance model. Extensive experiments on the popular benchmark datasets demonstrate that the proposed tracking approach performs favorably against several other state-of-the-art tracking algorithms. [ABSTRACT FROM AUTHOR]