Monitoring pilots’ mental states during training is important for ensuring safety and optimizing performance, yet existing full-cap EEG systems are impractical for operational use. In this work, we evaluate a headphone-style 9-channel electrode montage rather than a complete headphone EEG device, and propose a transfer learning framework to preserve performance under sparse coverage. Our two-stage approach first applies self-supervised pretraining on 64-channel full-cap EEG, and then adapts the learned representations to the headphone-style montage through a position correction module that accounts for electrode misplacement. We validate the framework in a flight simulator with 12 participants on two tasks: motor intention (left vs. right) and cognitive workload (low vs. high). Despite limited coverage, the headphone-style montage achieves within-subject accuracies of 85% for motor imagery and 89% for workload, compared to full-cap performance of 88% and 91%. Cross-subject accuracies reached 66% and 73%, demonstrating generalizability across users. Ablation analyses show that both self-supervised pretraining and position correction independently improve performance, and together provide a 12% boost in cross-subject decoding accuracy. These findings highlight the feasibility of headphone-style EEG montages for practical pilot monitoring, while clarifying methodological limitations and the need for future real-time and user-centered evaluation.

Open-vocabulary object detection aims to detect novel categories that are independent from the base categories used during training. Most modern methods adhere to the paradigm of learning vision-language space from a large-scale multi-modal corpus and subsequently transferring the acquired knowledge to off-the-shelf detectors like Faster-RCNN. However, information attenuation or destruction may occur during the process of knowledge transfer due to the domain gap, hampering the generalization ability on novel categories. To mitigate this predicament, in this paper, we present a novel framework named BIND, standing for Bulit-IN Detector, to eliminate the need for module replacement or knowledge transfer to off-the-shelf detectors. Specifically, we design a two-stage training framework with an Encoder-Decoder structure. In the first stage, an image-text dual encoder is trained to learn region-word alignment from a corpus of image-text pairs. In the second stage, a DETR-style decoder is trained to perform detection on annotated object detection datasets. In contrast to conventional manually designed non-adaptive anchors, which generate numerous redundant proposals, we develop an anchor proposal network that generates anchor proposals with high likelihood based on candidates adaptively, thereby substantially improving detection efficiency. Experimental results on two public benchmarks, COCO and LVIS, demonstrate that our method stands as a state-of-the-art approach for open-vocabulary object detection.

Despite considerable similarities between multiple object tracking (MOT) and single object tracking (SOT) tasks, modern MOT methods have not benefited from the development of SOT ones to achieve satisfactory performance. The major reason for this situation is that it is inappropriate and inefficient to apply multiple SOT models directly to the MOT task, although advanced SOT methods are of the strong discriminative power and can run at fast speeds.In this paper, we propose a novel and end-to-end trainable MOT architecture that extends CenterNet by adding an SOT branch for tracking objects in parallel with the existing branch for object detection, allowing the MOT task to benefit from the strong discriminative power of SOT methods in an effective and efficient way. Unlike most existing SOT methods which learn to distinguish the target object from its local backgrounds, the added SOT branch trains a separate SOT model per target online to distinguish the target from its surrounding targets, assigning SOT models the novel discrimination. Moreover, similar to the detection branch, the SOT branch treats objects as points, making its online learning efficient even if multiple targets are processed simultaneously. Without tricks, the proposed tracker achieves MOTAs of 0.710 and 0.686, IDF1s of 0.719 and 0.714, on MOT17 and MOT20 benchmarks, respectively, while running at 16 FPS on MOT17.

End-to-end discriminative trackers improve the state of the art significantly, yet the improvement in robustness and efficiency is restricted by the conventional discriminative model, i.e., least-squares based regression. In this paper, we present DTT, a novel single-object discriminative tracker, based on an encoder-decoder Transformer architecture. By self- and encoder-decoder attention mechanisms, our approach is able to exploit the rich scene information in an end-to-end manner, effectively removing the need for hand-designed discriminative models. In online tracking, given a new test frame, dense prediction is performed at all spatial positions. Not only location, but also bounding box of the target object is obtained in a robust fashion, streamlining the discriminative tracking pipeline. DTT is conceptually simple and easy to implement. It yields state-of-the-art performance on four popular benchmarks including GOT-10k, LaSOT, NfS, and TrackingNet while running at over 50 FPS, confirming its effectiveness and efficiency. We hope DTT may provide a new perspective for single-object visual tracking.

In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accuracy and efficiency. The society of visual tracking, however, still needs to deal with the following difficulty on CF trackers: avoiding or eliminating the boundary effect completely, in the meantime, exploiting non-linear kernels and running efficiently. In this paper, we propose a fast kernelized correlation filter without boundary effect (nBEKCF) to solve this problem. To avoid the boundary effect thoroughly, a set of real and dense patches is sampled through the traditional sliding window and used as the training samples to train nBEKCF to fit a Gaussian response map. Non-linear kernels can be applied naturally in nBEKCF due to its different theoretical foundation from the existing CF trackers'. To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter. Two algorithms, ACSII and CCIM, are developed to significantly accelerate the calculation of kernel correlation matrices. ACSII and CCIM fully exploit the density of training samples and cyclic structure of bases, and totally run in space domain. The efficiency of CCIM exceeds that of the FFT counterpart remarkably in our task. Extensive experiments on six public datasets, OTB-2013, OTB-2015, NfS, VOT2018, GOT10k, and TrackingNet, show that compared to the CF trackers designed to relax the boundary effect, BACF and SRDCF, our nBEKCF achieves higher localization accuracy without tricks, in the meanwhile, runs at higher FPS.

Recent years have seen remarkable progress in the visual tracking domain. However, it remains a challenging task to estimate the scale of target efficiently and accurately. In this paper, we present a novel and high-performance scale estimation approach for tracking-by-detection framework. The proposed approach, named GPAS, formulates the scale estimation as a Gaussian process regression problem based on scale pyramid representation. In general, it enjoys the following there advantages. (i) Efficient. It only takes 2ms to estimate the scale of a target on a single CPU. (ii) Accurate. Without bells and whistles, its accuracy surpasses all previous hand-crafted features based scale estimation methods by large margins. (iii) Generic. It can be incorporated into any tracking-by-detection framework based trackers easily. Experiment results show that compared to the latest and classical scale estimation method, fDSST, our GPAS significantly improves the performance by 6.2% in mean distance precision, 8.9% in mean overlap precision, and 5.5% in mean AUC on 28 sequences of OTB2013 with significant scale variations.

In recent years, two types of trackers, namely correlation filter-based tracker (CF tracker) and structured output tracker (struck), have exhibited the state-of-the-art performance. However, there seems to be a lack of analytic work on their relations in the computer vision community. In this paper, we investigate two state-of-the-art CF trackers, i.e., spatial regularization discriminative correlation filter (SRDCF) and correlation filter with limited boundaries (CFLB), and struck, and reveal their relations. Specifically, after extending the CFLB to its multiple channel versions, we prove the relation between SRDCF and CFLB on the condition that the spatial regularization factor of SRDCF is replaced by the masking matrix of CFLB. We also prove the asymptotical approximate relation between SRDCF and struck on the conditions that the spatial regularization factor of SRDCF is replaced by an indicator function of object bounding box, the weights of SRDCF in its loss item are replaced by those of struck, the linear kernel is employed by struck, and the search region tends to infinity. The extensive experiments on public benchmarks OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we explain how detailed differences among SRDCF, CFLB, and Struck would give rise to slightly different performances on visual sequences.

In recent years, correlation filter based trackers (CF trackers) have received much attention because of their top performance. Most CF trackers, however, suffer from low frame-per-second (fps) in pursuit of higher localization accuracy by relaxing the boundary effect or exploiting the high-dimensional deep features. In order to achieve real-time tracking speed while maintaining high localization accuracy, in this paper, we propose a novel CF tracker, fdKCF*, which casts aside the popular acceleration tool, i.e., fast Fourier transform, employed by all existing CF trackers, and exploits the inherent high-overlap among real (i.e., noncyclic) and dense samples to efficiently construct the kernel matrix. Our fdKCF* enjoys the following three advantages. (i) It is efficiently trained in kernel space and spatial domain without the boundary effect. (ii) Its fps is almost independent of the number of feature channels. Therefore, it is almost real-time, i.e., 24 fps on OTB-2015, even though the high-dimensional deep features are employed. (iii) Its localization accuracy is state-of-the-art. Extensive experiments on four public benchmarks, OTB-2013, OTB-2015, VOT2016, and VOT2017, show that the proposed fdKCF* achieves the state-of-the-art localization performance with remarkably faster speed than C-COT and ECO.

Recent developments of Correlation Filter based trackers (CF trackers) have attracted much attention because of their top performance. However, the boundary effect imposed by the basic periodic assumption in their fast optimization seriously degrades the performance of CF trackers. Although there existed many recent works to relax the boundary effect in CF trackers, the cost was that they can not utilize the kernel trick to improve the accuracy further. In this paper, we propose a novel Gaussian Process Regression based tracker (GPRT) which is a conceptually natural tracking approach. Compared to all the existing CF trackers, the boundary effect is eliminated thoroughly and the kernel trick can be employed in our GPRT. In addition, we present two efficient and effective update methods for our GPRT. Experiments are performed on two public datasets: OTB-2013 and OTB-2015. Without bells and whistles, on these two datasets, our GPRT obtains 84.1% and 79.2% in mean overlap precision, respectively, outperforming all the existing trackers with hand-crafted features.