Generic visual object tracking is challenging due to various difficulties, e.g. scale variations and deformations. To solve those problems, we propose a novel multi-scale selective kernel module for tracking, which contains small-scale and large-scale branches to model the target at different scales and attention mechanism to capture the more effective appearance information of the target. In our module, we cascade multiple small-scale convolutional blocks as an equivalent large-scale branch to extract large-scale features of the target effectively. Besides, we present a hybrid strategy for feature selection to extract significant information from features of different scales. Based on the current excellent segmentation tracking framework, we propose a novel tracking network that leverages our module at multiple places in the up-sample phase to construct a more accurate and robust appearance model. Extensive experimental results show that our tracker outperforms other state-of-the-art trackers on multiple challenging benchmarks including VOT2018, TrackingNet, DAVIS-2017, and YouTube-VOS-2018 while achieves real-time tracking.

The appearance model of the target needs to be updated for online single object tracking. However, the variation of the observation can be caused by active appearance change of the target, or the occlusion from the background. For the former case, we should update the appearance model and for the latter, the current model should be preserved. In this paper, we distinguish these two cases and resist the impact from heavy occlusion by estimating the background in the scene with moving cameras, while retaining the adaptivity to stationary cameras at the same time. The proposed method formulates the background as a Gaussian model and the target is determined in a coarse-to-fine manner. Experimental results demonstrate that our method achieves competitive results in the sequences with appearance changes and outperforms the state-of-the-art algorithms in dealing with complex occlusions.

A scheme that allows LTE-U system to harmoniously coexist with WIFI system in 5GHz unlicensed band is proposed in this paper. On the basis of listen-before-talk (LBT) channel access mechanism and dynamic channel switching strategy, the proposed scheme effectively evades harmful interference or blocking effects, caused by LTE-U system occupying the channel for a long time, to WIFI system. In order to alleviate the fairness problem when only one channel is available, the idle duration of the channel access scheme is optimized to further enhance the coexistence efficiency between LTE-U and WIFI system. Theoretical analysis and experimental results show that the proposed scheme enables the LTE-U and WIFI system to coexist well in the same band and achieve a better system performance.

How to build a robust and accurate appearance model is a crucial problem in object tracking. However, in most existing tracking methods, the structures among the adjacent video frames and neighboring regions, which may be helpful to improve the representation capability of the appearance model, have not been fully exploited. In this paper, we propose a novel tracking method by taking into account these structures to represent the appearance model. First, we propose a novel graph-regularized structured support vector machine (GS-SVM) algorithm by combining manifold learning and structured learning. Then, the proposed GS-SVM algorithm is employed to build the appearance model and a novel tracking method is developed. This new tracker not only absorbs the advantage of structured learning that deals with the intermediate classification step existing in tracking-by-detection methods, but also exploits the geometry structures in the tracked results and neighboring regions. In addition, a hybrid update strategy is introduced to fit with the GS-SVM-based appearance model. The experimental results demonstrate that the proposed tracking algorithm can outperform several state-of-the-art tracking methods in the benchmark dataset.

Single object tracking, in which a target is often initialized manually in the first frame and then is tracked and located automatically in the subsequent frames, is a hot topic in computer vision. The traditional tracking-by-detection framework, which often formulates tracking as a binary classification problem, has been widely applied and achieved great success in single object tracking. However, there are some potential issues in this formulation. For instance, the boundary between the positive and negative training samples is fuzzy, and the objectives of tracking and classification are inconsistent. In this paper, we attempt to address the above issues from the fuzzy system perspective and propose a novel tracking method by formulating tracking as a fuzzy classification problem. First, we introduce the fuzzy strategy into tracking and propose a novel fuzzy tracking framework, which can measure the importance of the training samples by assigning different memberships to them and offer more strict spatial constraints. Second, we develop a fuzzy least squares support vector machine (FLS-SVM) approach and employ it to implement a concrete tracker. In particular, the primal form, dual form, and kernel form of FLS-SVM are analyzed and the corresponding closed-form solutions are derived for efficient realizations. Besides, a least squares regression model is built to control the update adaptively, retaining the robustness of the appearance model. The experimental results demonstrate that our method can achieve comparable or superior performance to many state-of-the-art methods.

How to build an accurate and reliable appearance model to improve the performance is a crucial problem in object tracking. Since the multi-view learning can lead to more accurate and robust representation of the object, in this paper, we propose a novel tracking method via multi-view learning framework by using multiple support vector machines (SVM). The multi-view SVMs tracking method is constructed based on multiple views of features and a novel combination strategy. To realize a comprehensive representation, we select three different types of features, i.e., gray scale value, histogram of oriented gradients (HOG), and local binary pattern (LBP), to train the corresponding SVMs. These features represent the object from the perspectives of description, detection, and recognition, respectively . In order to realize the combination of the SVMs under the multi-view learning framework, we present a novel collaborative strategy with entropy criterion, which is acquired by the confidence distribution of the candidate samples. In addition, to learn the changes of the object and the scenario, we propose a novel update scheme based on subspace evolution strategy. The new scheme can control the model update adaptively and help to address the occlusion problems . We conduct our approach on several public video sequences and the experimental results demonstrate that our method is robust and accurate, and can achieve the state-of-the-art tracking performance.