A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Yin, Wenji; Peng, Yueping; Ye, Zecong; Liu, Wenchao

doi:10.3390/app132111651

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…Drones have become readily accessible and extensively employed in various fields, including mapping [1], security [2,3], agriculture [4], express delivery [5], and numerous others [6]. In the forthcoming years, the use of autonomous and intelligent drones is expected to rise exponentially.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection

Ye,

Peng,

Liu

et al. 2024

Drones

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With the continuous advancement of drone technology, drones are demonstrating a trend toward autonomy and clustering. The detection of airborne objects from the perspective of drones is critical for addressing threats posed by aerial targets and ensuring the safety of drones in the flight process. Despite the rapid advancements in general object detection technology in recent years, the task of object detection from the unique perspective of drones remains a formidable challenge. In order to tackle this issue, our research presents a novel and efficient mechanism for adjacent frame fusion to enhance the performance of visual object detection in airborne scenarios. The proposed mechanism primarily consists of two modules: a feature alignment fusion module and a background subtraction module. The feature alignment fusion module aims to fuse features from aligned adjacent frames and key frames based on their similarity weights. The background subtraction module is designed to compute the difference between the foreground features extracted from the key frame and the background features obtained from the adjacent frames. This process enables a more effective enhancement of the target features. Given that this method can significantly enhance performance without a substantial increase in parameters and computational complexity, by effectively leveraging the feature information from adjacent frames, we refer to it as an efficient adjacent frame fusion mechanism. Experiments conducted on two challenging datasets demonstrate that the proposed method achieves superior performance compared to existing algorithms.

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Section: Introductionmentioning

confidence: 99%

An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection

Ye,

Peng,

Liu

et al. 2024

Drones

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“…Vehicle re-identification (ReID) has increasingly become a focal task due to its significant applications in intelligent transportation systems [1][2][3][4][5]. The aim of vehicle ReID lies in recognizing all instances of a specific vehicle across various camera viewpoints.…”

Section: Introductionmentioning

confidence: 99%

RANDnet: Vehicle Re-Identification with Relation Attention and Nuance–Disparity Masks

Huang,

Sheng,

2024

Applied Sciences

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Vehicle re-identification (vehicle ReID) is designed to recognize all instances of a specific vehicle across various camera viewpoints, facing significant challenges such as high similarity among different vehicles from the same viewpoint and substantial variance for the same vehicle across different viewpoints. In this paper, we introduce the RAND network, which is equipped with relation attention mechanisms, nuance, and disparity masks to tackle these issues effectively. The disparity mask specifically targets the automatic suppression of irrelevant foreground and background noise, while the nuance mask reveals less obvious, sub-discriminative regions to enhance the overall feature robustness. Additionally, our relation attention module, which incorporates an advanced transformer architecture, significantly reduces intra-class distances, thereby improving the accuracy of vehicle identification across diverse viewpoints. The performance of our approach has been thoroughly evaluated on widely recognized datasets such as VeRi-776 and VehicleID, where it demonstrates superior effectiveness and competes robustly with other leading methods.

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A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Cited by 2 publications

References 54 publications

An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection

An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection

RANDnet: Vehicle Re-Identification with Relation Attention and Nuance–Disparity Masks

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