In visual tracking, sometimes the target response value is high, but it is not the tracking result, which can result in the wrong judgment. Moreover, the threshold to decide the tracking result needs to be set artificially in the traditional discriminative methods. We propose a deep learning-based target drift discriminative network to judge whether the target is lost. We design a lightweight network without the threshold, using four convolutional layers, three full connection layers, and the Softmax function to judge the tracking results. When training the network, the established positive and negative samples are used, and we select difficult samples for further training to achieve a better target discriminative effect. Finally, a target drift discriminative network is introduced into the accurate tracking by overlap maximization. When it is judged that the target is lost, another search area is selected to quickly find the target. Numerous experiments show that our method achieves the best performance on datasets UAV123, UAV20L, and VOT2018-LT, especially on the UAV20L dataset, for which the tracking precision and tracking success rate are improved by 3.7% and 2.8%. Compared with several other classical threshold discriminative criteria, we do not need to set the threshold artificially and have better judgment performance.
To fully develop the complementary advantages of different visual features and to improve the robustness of multi-feature fusions, we propose a robust correlation filter tracker with adaptive multi-complementary features fusion based on game theory. By combining the complementary features selected from handcrafted features and convolution features, our method constructs two robust combined features in the tracking framework of discriminative correlation filters (DCFs). In addition, by utilizing game theory, the two combined features are regarded as two sides of the game, achieving the best balance through continuous gaming throughout the tracking process and thus obtaining a more robust fused feature. The experimental results obtained on the OTB2015 benchmark dataset demonstrate that our tracker improves the robustness of object tracking in complex scenarios, such as occlusion and deformation, and performs favorably against eight state-of-the-art methods.
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%.
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.