Xiangyang Yang

Xucheng Wang, Xiangyang Yang, Hengzhou Ye et al.

Efficiency has been a critical problem in UAV tracking due to limitations in computation resources, battery capacity, and unmanned aerial vehicle maximum load. Although discriminative correlation filters (DCF)-based trackers prevail in this field for their favorable efficiency, some recently proposed lightweight deep learning (DL)-based trackers using model compression demonstrated quite remarkable CPU efficiency as well as precision. Unfortunately, the model compression methods utilized by these works, though simple, are still unable to achieve satisfying tracking precision with higher compression rates. This paper aims to exploit disentangled representation learning with mutual information maximization (DR-MIM) to further improve DL-based trackers' precision and efficiency for UAV tracking. The proposed disentangled representation separates the feature into an identity-related and an identity-unrelated features. Only the latter is used, which enhances the effectiveness of the feature representation for subsequent classification and regression tasks. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that our DR-MIM tracker significantly outperforms state-of-the-art UAV tracking methods.

You Wu, Xiangyang Yang, Xucheng Wang et al.

Harnessing low-light enhancement and domain adaptation, nighttime UAV tracking has made substantial strides. However, over-reliance on image enhancement, limited high-quality nighttime data, and a lack of integration between daytime and nighttime trackers hinder the development of an end-to-end trainable framework. Additionally, current ViT-based trackers demand heavy computational resources due to their reliance on the self-attention mechanism. In this paper, we propose a novel pure Mamba-based tracking framework (MambaNUT) that employs a state space model with linear complexity as its backbone, incorporating a single-stream architecture that integrates feature learning and template-search coupling within Vision Mamba. We introduce an adaptive curriculum learning (ACL) approach that dynamically adjusts sampling strategies and loss weights, thereby improving the model's ability of generalization. Our ACL is composed of two levels of curriculum schedulers: (1) sampling scheduler that transforms the data distribution from imbalanced to balanced, as well as from easier (daytime) to harder (nighttime) samples; (2) loss scheduler that dynamically assigns weights based on the size of the training set and IoU of individual instances. Exhaustive experiments on multiple nighttime UAV tracking benchmarks demonstrate that the proposed MambaNUT achieves state-of-the-art performance while requiring lower computational costs. The code will be available at https://github.com/wuyou3474/MambaNUT.

Xiangyang Yang, Xuefeng Guan, Lanxue Dang et al.

Efficient spatial indexing is crucial for processing large-scale spatial data. Traditional spatial indexes, such as STR-Tree and Quad-Tree, organize spatial objects based on coarse approximations, such as their minimum bounding rectangles (MBRs). However, this coarse representation is inadequate for complex spatial objects (e.g., district boundaries and trajectories), limiting filtering accuracy and query performance of spatial indexes. To address these limitations, we propose GP-Tree, a fine-grained spatial index that organizes approximated grid cells of spatial objects into a prefix tree structure. GP-Tree enhances filtering ability by replacing coarse MBRs with fine-grained cell-based approximations of spatial objects. The prefix tree structure optimizes data organization and query efficiency by leveraging the shared prefixes in the hierarchical grid cell encodings between parent and child cells. Additionally, we introduce optimization strategies, including tree pruning and node optimization, to reduce search paths and memory consumption, further enhancing GP-Tree's performance. Finally, we implement a variety of spatial query operations on GP-Tree, including range queries, distance queries, and k-nearest neighbor queries. Extensive experiments on real-world datasets demonstrate that GP-Tree significantly outperforms traditional spatial indexes, achieving up to an order-of-magnitude improvement in query efficiency.