Implementing robust and real-time visual tracking method is a challenging task due to many disturbing factors such as illumination changes, appearance changes, noises and irregular dynamic occlusion, etc. In order to solve this problem, a moving target tracking method, which can update the background template real-time with memory and establish self-adaptive background template library, is presented in this paper. Firstly, erosion processing, boundary tracing, sparse processing and full-matrix operating are used to determine moving target in an image. Secondly, a self-adaptive template library is established. Finally, the background template is updated at the same time. Experimental results show that the proposed method can achieve effective tracking task with real-time requirement, and can well respond to many nature interferences.

Tracking method of moving target in complex environment

Local appearance representation of tracking objects plays a key role in robust visual tracking. While, holistic appearance representation mostly dominates the feature extraction model when describing the target appearance in existing DNN-based trackers, leading to high sensitivity to appearance variation, such as rotation, appearance change, and partial occlusion. We introduce a spatial transformer local part detector, which provides more robust local representation for tracking object and combine the existing Siamese networks for visual tracking simultaneously. To this end, we explicit the spatial transformer networks in the local part detector to detect the discriminative region of tracking objects. The local part detector further passes the detection results to the local cropping module, which comes up with the local structure for the region of interest. Differentiable operations can perform end-to-end learning during local structure construction. We implement the tracking procedure by matching the combined local pattern between candidates and templates in Siamese networks. We extensively prove the effectiveness of the proposed method through the ablation studies of the tracking benchmark, including OTB-2015, VOT-2018, and UAV123.

Spatial Transformer Part-based Siamese Visual Tracking

Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

Adaptive learning compressive tracking based on Markov location prediction

Compared with a short-term task, long-term tracking has received more attention and research in recent years. Long-term tracking is more challenging because it needs to solve two difficult problems: when to update and how to update our model. Many outstanding short-term tracking methods update frame by frame or manually set the threshold to judge if the tracker should be updated, but when the target is blocked or escapes from the field of view, it is easy to get and update wrong samples, resulting in model pollution and drift. Not only that, but due to the lack of a re-detection mechanism, it is difficult for these short-term tracking methods to recover once the target is lost (especially when the target reappears from another location). In this work, we propose a high-speed long-term tracker with adaptive occlusion and recovery judgment (LT-AOR), which comprehensively judge the update chance of the tracker through the discrimination information and appearance information, and re-detects the target in a simplified way to achieve stable tracking in the case of target occlusion and loss.

Long-term Tracker with Adaptive Occlusion and Recovery Judgment

Due to the complexity of Unmanned Aerial Vehicle (UAV) target tracking scenarios, tracking drift caused by target occlusion is common and has no suitable solution. In this paper, an occlusion-resistant target tracking algorithm based on the correlated filter tracking model is proposed. First, instead of the traditional target tracking model that uses single template matching to locate the target, we locate the target by finding the optimal match based on multiple candidates templates matching. Then, in order to increase the accuracy of matching, we use the self-attentive mechanism for feature enhancement. We experiment our proposed algorithm on datasets OTB100 and UAV123, respectively, and the results show that the tracking accuracy of our algorithm outperforms the traditional correlated filtered target tracking model. In addition, we have also tested the anti-occlusion performance of our proposed algorithm on some video sequences in which the target is occluded. The results show that our proposed algorithm has a certain resistance to occlusion, especially in the UAV tracking scenario.

/pdf/correlation-filter-of-multiple-candidates-match-for-anti-1f2c7tzh.pdf

Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario

Recent advances in visual tracking showed that deep Convolutional Neural Networks(CNN) trained for image classification can be strong feature extractors for discriminative trackers. However, due to the long-tail of categories, occlusions, deformations and some other attributes are so rare that they will hardly happen. Yet we want to learn a model invariant to such occurrences for fear that we could handle complex attributes in tracking procedure. In this paper, we propose an alternative solution. We propose to learn an Adversarial Fusion Networks(AFN) that generates examples with occlusions and deformation based on the internal structure of Region Proposal Network (RPN). We discovered that the internal structure of Adversarial Fusion Networks(AFN)'s top layer feature can be utilized for robust visual tracking. We illustrated that such networks can be more robust when the tracking object suffering from occlusion and deformation. Without ensemble and any extra treatment on feature maps, our proposed method achieved state-of-the-art results on several large scale benchmarks including OTB50, OTB100 and VOT2016.

Robust Visual Tracking Based on Adversarial Fusion Networks

As the development of Artificial Intelligent, visual object tracking plays a key role in computer vision area with numerous real-world applications. This paper proposes a novel approach with the combination of deep spatial transformer features and discriminative correlation filters-based tracking frameworks for the visual tracking problem. The deep spatial transformer features have several advantages compared to the standard deep features (fully connected layers). Firstly, they are more robust when object is suffering affine transformation because of the specific characteristic. Secondly, they have low dimensionality with deeper layer. Lastly, some structural information is contained during tracking procedure. We perform comprehensive experiments on two benchmark datasets: OTB and VOT2015. Surprisingly, compared with some traditional approach based on hand-crafted features, our results further show the affine robustness. We also maintain the accuracy and tracking speed compared with several discriminative correlation filter-based tracker at the same time. Finally, results comparable to state-of-the-art trackers are obtained on all two benchmark datasets.

Robust Visual Tracking based on Deep Spatial Transformer Features

As the development of Artificial Intelligent, computer vision has became one of the most important elements of all the technologies which composed the AI system, especially robot. Object tracking plays a key role in computer vision. While, there still remain some unsolved problems when the target suffering occlusion, illumination, scale change and rotation. The proposed tracking algorithm obtain the appearance model using the theory of compressive sensing, A LS-SVM classifier if used to separate the positive templates from negative samples. Then, we design a hypergraph propagation method to capture the contextual information on samples in order to improve the tracking accuracy. Updating scheme makes the algorithm more adaptive. Experimental results have proved the effectiveness and robustness of the proposed tracker.

Compressive tracking using incremental LS-SVM

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Robust Visual Tracking Based on Adversarial Fusion Networks

Robust Visual Tracking based on Deep Spatial Transformer Features

Compressive tracking using incremental LS-SVM