Xiaoxiaong Zhang’s scientific contributions

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Publications (1)


Fig. 2: System diagram of the proposed DBCFs algorithm for VOT. In forward and backward tracking processes, the deep features are first computed in steps (a)-(c) and then DCFs is trained to get both forward and backward filters shown in step (d). Steps (f)-(h) depicts the VOT process. In case of forward tracking, the target is tracked from past frames to the current frame (step (h)) while in the case of backward tracking the target is tracked from future frames to the current frame (step (h)). Step (i) shows the computation of the appearance consistency lose to obtain the resulting response map for continuous VOT.
Deep Bidirectional Correlation Filters for Visual Object Tracking
  • Conference Paper
  • Full-text available

May 2020

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174 Reads

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5 Citations

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Xiaoxiaong Zhang

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Visual Object Tracking (VOT) is an essential task for many computer vision applications. VOT becomes challenging when a target object faces severe occlusion, drastic illumination changes, and scale variation problems. In the literature, Discriminative Correlation Filters (DCFs)-based tracking methods have achieved promising results in terms of accuracy and efficiency in many complex VOT scenarios. A plethora of DCFs trackers have been proposed which exploit information observed in past frames to create and update DCFs for VOT. To adapt to target appearance variations, the DCFs are enhanced by incorporating spatial and temporal consistency constraints. Nevertheless, the performance degradation is observed for these methods because of the aforementioned limitations. To address these issues, we propose a novel algorithm based on bidirectional DCFs for VOT. In this algorithm, we propose the original idea of leveraging information from both past and future frames. The proposed algorithm first tracks the target object forward in the video sequence and then its uses the predicted location of the last window frame and track the target object backward towards the current frame. We design an appearance consistency loss function by taking the L2 norm between the regression target of the forward tracking and response map of the backward tracking to obtain the resulting response map. Our proposed algorithm realizes a highly accurate DCFs because forward and backward tracking information are fused together for consistent VOT. Although, a result will be output with some small delay because information is taken from a future to the present period, our proposed algorithm has the merit of addressing the drastic appearance variations VOT challenges. We evaluate our proposed tracker using deep features on three publicly available challenging datasets. Our results demonstrate the superior performance of the proposed tracker compared to the existing state-of-the-art trackers.

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Citations (1)


... Dai et al. [19] presented adaptive regularization in a correlation filter which can learn and update the target model according to appearance variations during tracking. Javed et al. [20] proposed a deep correlation filter-based tracking method, by utilizing both forward and backward tracking information between the regression target and response map. Despite the fact that deep learning-based methods achieve favorable results, the complexity of these methods is still higher with the requirement of offline training. ...

Reference:

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking
Deep Bidirectional Correlation Filters for Visual Object Tracking