For robust and effective tracking, most efforts strive to design a powerful representation target model, while we are inspired by the idea of“knowing oneself and knowing others”to major in both the target and non-target features. In this work, we propose a unit correlation with interactive feature tracker (UCIF), which utilizes feature interaction and independent correlation operation to improve robustness and effectiveness. Specifically, we first propose a feature integration network, in which the feature enhancement module concentrates on enhancing the tracker's representation ability for both target and non-target. The feature interaction module is in charge of completing the interactive learning between target and non-target features. Then, considering the potential risk of blurring spatial information in regular correlation operation, a unit correlation network is presented, where the convolution sampling strategy can integrate the target features as well as reduce the computation costs. The unit kernel for correlation operation can protect the target spatial information. The channel ranking module suppresses background interference via weight assignment. Extensive experiments are conducted on both the short-term and long-term challenging benchmarks, including OTB2015, NFS, UAV123, TrackingNet, GOT-10 k, TLP, LaSOT and VOT-LT2019. Our tracker achieves remarkable performance in robustness and effectiveness.

Adversarial attacks in visual object tracking aim to drop tracking performance through injecting imperceptible perturbations to the input of the tracker. Current methods usually superimpose perturbation maps on the input images, and advocate blinding the tracker via occluding the real targets to achieve attack effect. From the perspective of attraction, we alternatively propose a novel idea of attacking the tracker, which advocates using perturbation patches to act as fake targets to attract the tracker's attention. For this purpose, we establish a multi-conditional objective function to generate our ideal patch in an offline iterative manner. For invisibility, we integrate the constraint of patch value into the function for unified optimization. For universality, in addition to adopting large-scale and high-diversity training samples , we also incorporate the video-agnostic condition into this function. To make the patch attractive like a fake target, we elaborately design the non-overlapping area to determine the patch position, and generate matched fake labels to mislead the tracker to track the patch. In online attacking, it only needs to paste the optimized patch onto the video frames, the tracker will be successfully attracted by our patch, achieving attack effect. Extensive experimental results on 8 popular tracking datasets demonstrate that our method can obtain exceptional attack performance in both non-targeted and targeted attack. Additionally, the experiments on transferability illustrate our optimized patches can be directly applied to other trackers with different architectures.

Adversarial attacks in visual object tracking aims to fool trackers via injecting invisible perturbations for the video frames. Most adversarial methods advocate generating perturbations for each video frame, but frequent attacks may increase the computational load and the risk of exposure. Unfortunately, less works are about only attacking the initial frame and their attack effects are insufficient. To tackle this, we focus on the initialization phase of tracking and propose an only once attack framework. It can effectively fool the tracker via only generating invisible perturbations for the initial template, rather than each frame. Specifically, considering the tracking mechanism of the Siamese-based trackers, we design the minimum score-based and the minimum IoU-based loss functions. Both of them are used for training the UNet-based perturbation generator instead of the tracker, achieving the non-targeted attack. Additionally, we propose the location and direction offsets as the base attacks of sophisticated targeted attack. Combined with the two basic attacks, the tracker can be easily hijacked to move towards the fake target predefined by users. Extensive experimental results demonstrate that our only once attack framework costs the least number of attacks yet achieves better attack effect, with the maximum drop of 68.7%. The transferability experiments illustrate that our attack framework with good generalization ability can be directly applicable to the CNN-based, Siamese-based, deep discriminative-based and Transformer-based trackers, without retraining.

As a challenging visual task, visual object tracking has recently been composed of the classification and regression subtasks. The anchor-free regression network gets rid of the dependence on the anchors, but the redundant range makes it usually regress some samples involving non-target information. Evenly dividing a target by the regular receptive field often causes ambiguous target localization. To address these issues, we propose a regression-selective feature-adaptive tracker (RSFA), where the regression-selective subnetwork can not only free the regression task from anchors, but can also select more effective regression samples using the refined criterion. The proposed feature-adaptive strategy concentrates the classification subnetwork on target feature extraction via adaptively modifying the receptive field, and the attached centrality branch offers a correction for target localization by exploiting the spatial information. Additionally, the designed online update mechanism realizes the tracker's online optimization, improving robustness against target deformation. Extensive experiments are conducted on challenging benchmarks, including GOT10 K, OTB2015, UAV123, NFS, VOT2018, VOT2019 and VOT2020-ST. Our tracker achieves satisfactory tracking results, and the evaluations of its tracking performance rank first or second in comparison with the state-of-the-art tracking algorithms.