TATrack: Target-aware transformer for object tracking
7
Citation
41
Reference
10
Related Paper
Citation Trend
Keywords:
Discriminative model
BitTorrent tracker
Margin (machine learning)
Though not often mentioned, the price point of many eye tracking systems may be a factor limiting their adoption in research. Recently, several inexpensive eye trackers have appeared on the market, but to date little systematic research has been conducted to validate these systems. The present experiment attempted to address this gap by evaluating and comparing five different eye trackers, the Eye Tribe Tracker, Tobii EyeX, Seeing Machines faceLAB, Smart Eye Pro, and Smart Eye Aurora for their gaze tracking accuracy and precision. Results suggest that all evaluated trackers maintained acceptable accuracy and precision, but lower cost systems frequently also experienced high rates of data loss, suggesting that researchers adopting low cost systems such as those evaluated here should be judicious in their research usage.
BitTorrent tracker
Limiting
Tracking (education)
Cite
Citations (75)
Recently, great progesses have been made in using discriminative classifiers in object tracking. More specifically, correlation filters (CFs) for visual tracking have been attractive due to t heir competitive performances on both accuracy and robustness. In this paper, the latest and representative approaches of CF based trackers are presented in detail. In addition, trackers used deep convolutional features are introduced and several famous tracking methods which fine-tune the pretrained deep network are presented. To evaluate the performances of different trackers, a detailed introduction of the evaluation methodology and the datasets is described, and all introduced trackers are compared based on the mentioned datasets. Finally, several promising directions as the conclusions are drawn in this paper.
BitTorrent tracker
Discriminative model
Robustness
Tracking (education)
Cite
Citations (0)
Discriminative model
BitTorrent tracker
Cite
Citations (2)
Maintaining high efficiency and high precision are two fundamental challenges in UAV tracking due to the constraints of computing resources, battery capacity, and UAV maximum load. Discriminative correlation filters (DCF)-based trackers can yield high efficiency on a single CPU but with inferior precision. Lightweight Deep learning (DL)-based trackers can achieve a good balance between efficiency and precision but performance gains are limited by the compression rate. High compression rate often leads to poor discriminative representations. To this end, this paper aims to enhance the discriminative power of feature representations from a new feature-learning perspective. Specifically, we attempt to learn more disciminative representations with contrastive instances for UAV tracking in a simple yet effective manner, which not only requires no manual annotations but also allows for developing and deploying a lightweight model. We are the first to explore contrastive learning for UAV tracking. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that the proposed DRCI tracker significantly outperforms state-of-the-art UAV tracking methods.
Discriminative model
BitTorrent tracker
Tracking (education)
Feature (linguistics)
Cite
Citations (0)
Discriminative Correlation Filters (DCFs)-based approaches have recently achieved competitive performance in visual tracking. However, such conventional DCF-based trackers often lack the discriminative ability due to the shallow architecture. As a result, they can hardly tackle drastic appearance variations and easily drift when the target suffers heavy occlusions. To address this issue, a novel densely connected DCFs framework is proposed for visual tracking. We incorporate multiple nested DCFs into the deep learning architecture, and then train the compact network with the data-specific target. Specifically, feature maps and interim response maps are shared and reused throughout the whole network. By doing so, the implicit information carried out by each DCF is fully exploited to enhance the model representation ability during the tracking process. Moreover, a multiscale estimation scheme is developed to account for scale variations. Experimental results on the benchmarks demonstrate that the proposed approach achieves outstanding performance compared to the existing state-of-the-art trackers.
Discriminative model
BitTorrent tracker
Tracking (education)
Representation
Feature (linguistics)
Cite
Citations (5)
Discriminative model
BitTorrent tracker
Tracking (education)
Active appearance model
Generative model
Cite
Citations (2)
The development of eye tracking-based applications has witnessed a number of advancements over the past few years. As a result, a number of low cost commercial remote vision-based eye trackers started to appear in the market. Consequently, a number of research communities started to explore the feasibility of extending the eye-tracking capabilities beyond single computer screen and utilize it in multi-screen setup. One of the main challenges for the wide adoption of such eye trackers in multi-screen setup, is their limitations when it comes to an intuitive and reliable way for tracking human eye movements across these multiple screens without losing much of the eye tracking data itself. In this work, a novel data-driven approach based on deep recurrent neural networks for a reliable and responsive switching mechanism between low cost multi-screen eye trackers is proposed. Our approach has achieved a competent results in terms of higher accuracy and lower positive rate in detecting accurately the screen the subject is attending to with F1 measure score of 85%.
BitTorrent tracker
Tracking (education)
Cite
Citations (0)
Discriminative model
BitTorrent tracker
Feature (linguistics)
Tracking (education)
Cite
Citations (8)
Maintaining high efficiency and high precision are two fundamental challenges in UAV tracking due to the constraints of computing resources, battery capacity, and UAV maximum load. Discriminative correlation filters (DCF)-based trackers can yield high efficiency on a single CPU but with inferior precision. Lightweight Deep learning (DL)-based trackers can achieve a good balance between efficiency and precision but performance gains are limited by the compression rate. High compression rate often leads to poor discriminative representations. To this end, this paper aims to enhance the discriminative power of feature representations from a new feature-learning perspective. Specifically, we attempt to learn more disciminative representations with contrastive instances for UAV tracking in a simple yet effective manner, which not only requires no manual annotations but also allows for developing and deploying a lightweight model. We are the first to explore contrastive learning for UAV tracking. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that the proposed DRCI tracker significantly outperforms state-of-the-art UAV tracking methods.
Discriminative model
BitTorrent tracker
Tracking (education)
Feature (linguistics)
Cite
Citations (5)
BitTorrent tracker
Scope (computer science)
Tracking (education)
Tracking system
Cite
Citations (2)