Prof. Xinxi Feng Profile

Prof. Xinxi Feng

at Air Force Engineering Univ.

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Publications (2)

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Proceedings Article | 27 January 2021 Paper

Adaptive context-aware correlation filter tracker with multiple detection regions

Lei Pu, Xinxi Feng, Zhiqang Hou, Wangsheng Yu

Proceedings Volume 11720, 117200L (2021) https://doi.org/10.1117/12.2589424

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Correlation filter based tracking algorithms have recently shown favorable performance in terms of high frame rates. However, a significant problem is that the context information is not be fully used which can result in model drift under challenging situations, such as fast motion and occlusion. In this paper, we propose an adaptive context-aware correlation framework which can improve the discriminative power and detect target within a large neighborhood. Firstly, we construct a context-aware correlation filter model and a peak extraction method is proposed to select the context patches adaptively, which can be regarded as hard negative samples mining. Secondly, a simple yet effective multi-region detection strategy is proposed to improve the anti-occlusion ability and prevent model drift. Thirdly, we adopt high-confidence model update method to avoid model corruption. We integrate the proposed framework with the existing DCF tracker, experimental results show that the proposed framework improves the accuracy by 9.1% and the success rate by 7.1%.

Proceedings Article | 6 May 2019 Paper

Covariance intersection fusion for visual tracking with hierarchical features

Lei Pu, Xinxi Feng, Zhiqiang Hou

Proceedings Volume 11069, 1106941 (2019) https://doi.org/10.1117/12.2524244

KEYWORDS: Detection and tracking algorithms, Optical tracking, Image filtering, Electronic filtering, Machine vision, Computer vision technology, Visualization, Feature extraction, Performance modeling, Visual process modeling

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In recent years, several visual tracking methods have applied multilayer convolutional features to correlation filters, but they mostly use fixed weights to fuse the multilayer response maps, which is difficult to adapt to various scene changes. To address this problem, a robust tracking algorithm based on adaptive fusion of multilayer response maps is proposed. In this paper, we extract multilayer convolutional features from the target’s candidate area to improve the tracking robustness and the translation correlation filter is feed with CNN features extracted from each layer. Different from previous methods, we proposed a fast covariance intersection algorithm to adaptive fuse the multilayer response maps. After the final target center position is determined, we adopted a 1D scale filter through multi-scale sampling with HOG features to handle large scale variations. Moreover, in order to solve the problem of tracking drifts due to the severe occlusion and error accumulation, we present a new random update mechanism to update the translation filters. The experimental results on some challenging benchmark datasets show that the proposed algorithm achieves the outstanding performance against the state-of-the-art tracking methods.

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