Enhancing UAV tracking: a focus on discriminative representations using contrastive instances

Wang，Xucheng1; Zeng，Dan2; Li，Yongxin1; Zou，Mingliang1; Zhao，Qijun3; Li，Shuiwang1

2024-05-01

10.1007/s11554-024-01456-2

Journal of Real-Time Image Processing   影响因子和分区

1861-8200

1861-8219

Addressing the core challenges of achieving both high efficiency and precision in UAV tracking is crucial due to limitations in computing resources, battery capacity, and maximum load capacity on UAVs. Discriminative correlation filter (DCF)-based trackers excel in efficiency on a single CPU but lag in precision. In contrast, many lightweight deep learning (DL)-based trackers based on model compression strike a better balance between efficiency and precision. However, higher compression rates can hinder performance by diminishing discriminative representations. Given these challenges, our paper aims to enhance feature representations’ discriminative abilities through an innovative feature-learning approach. We specifically emphasize leveraging contrasting instances to achieve more distinct representations for effective UAV tracking. Our method eliminates the need for manual annotations and facilitates the creation and deployment of lightweight models. As far as our knowledge goes, we are the pioneers in exploring the possibilities of contrastive learning in UAV tracking applications. Through extensive experimentation across four UAV benchmarks, namely, UAVDT, DTB70, UAV123@10fps and VisDrone2018, We have shown that our DRCI (discriminative representation with contrastive instances) tracker outperforms current state-of-the-art UAV tracking methods, underscoring its potential to effectively tackle the persistent challenges in this field.

Contrastive instances Contrastive learning Discriminative representation UAV tracking

SCI ; EI

英语

其他

2-s2.0-85190891226

Scopus

1.College of Computer Science and Engineering,Guilin University of Technology,Guilin,541000,China
2.Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518000,China
3.College of Computer Science,Sichuan University,Chengdu,30332,China

Wang，Xucheng,Zeng，Dan,Li，Yongxin,et al. Enhancing UAV tracking: a focus on discriminative representations using contrastive instances[J]. Journal of Real-Time Image Processing,2024,21(3).

Wang，Xucheng,Zeng，Dan,Li，Yongxin,Zou，Mingliang,Zhao，Qijun,&Li，Shuiwang.(2024).Enhancing UAV tracking: a focus on discriminative representations using contrastive instances.Journal of Real-Time Image Processing,21(3).

Wang，Xucheng,et al."Enhancing UAV tracking: a focus on discriminative representations using contrastive instances".Journal of Real-Time Image Processing 21.3(2024).