Attention Mask-Based Network With Simple Color Annotation for UAV Vehicle Re-Identification

Yao, Aihuan; Huang, Mengmeng; Qi, Jiahao; Zhong, Ping

doi:10.1109/lgrs.2021.3092369

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…The design of the loss function is used to improve the model sampling method, which in turn improves the performance of the vehicle Re-ID model in UAV scenarios. Yao et al 40 introduced a weighted triplet loss (WTL) function to penalize the embedded features of larger strength negative pairs, which is well targeted for the training of UAV vehicle Re-ID networks. Besides, the normalized softmax loss 41 is proposed to increase the inter-class distance and decrease the intra-class distance and combine with the triplet loss to train the model, which solves the problem of how to robustly learn a common visual representation of vehicles from different viewpoints and distinguish between different vehicles with similar visual appearance by optimizing the loss function.…”

Section: Related Work On the Vehicle Re-id Taskmentioning

confidence: 99%

A novel dual-pooling attention module for UAV vehicle re-identification

Guo,

Yang,

Jia

et al. 2024

Sci Rep

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Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

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Section: Related Work On the Vehicle Re-id Taskmentioning

confidence: 99%

A novel dual-pooling attention module for UAV vehicle re-identification

Guo,

Yang,

Jia

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On the UAV-VeID dataset, the methods of comparison include [1,22,[30][31][32]35,[39][40][41][42][43][44]. Table 1 compares our proposed DMANet to other methods in the UAV-VeID dataset.…”

Section: Experiments On Uav-veidmentioning

confidence: 99%

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

Yin,

Peng,

et al. 2023

Applied Sciences

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Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly.

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“…Therefore, it is necessary to perform UAV path planning when utilizing UAVs to obtain surface information of target buildings. Traditional UAV path planning methods mainly focus on applications such as searching or tracking of ground targets [5][6][7], and shortest path planning under certain conditions [8,9], while there is a lack of research on how to efficiently obtain the surface information of targets. Efficient information acquisition can obtain a variety of attribute information of the target building, such as appearance texture, geographical location, and height; additionally, it is considerably beneficial to obtain surface information for 3D reconstruction and object detection.…”

Section: Introductionmentioning

confidence: 99%

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

Chen,

Liang,

Meng

2023

Remote Sensing

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To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.

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Attention Mask-Based Network With Simple Color Annotation for UAV Vehicle Re-Identification

Cited by 11 publications

References 13 publications

A novel dual-pooling attention module for UAV vehicle re-identification

A novel dual-pooling attention module for UAV vehicle re-identification

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

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

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