An Asymmetric Feature Enhancement Network for Multiple Object Tracking of Unmanned Aerial Vehicle

Abstract

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

Remote Sensing Image Dehazing via a Local Context-Enriched Transformer

“…Remote sensing images captured by the satellite or unmanned aerial vehicle are degraded by the existing haze or cloud [1][2][3][4], which destroys the surface information acquisition and further degrades the downstream tasks including image classification [5][6][7], object detection [8][9][10], change detection [11,12], object tracking [13,14], image segmentation [15,16], and so on. Remote image dehazing methods are to recover the clean image from its haze or cloud-polluted variants, which could be applied in applications with environment monitoring, military surveillance, and so on.…”