A new adaptive feature fusion and selection network for intelligent transportation systems

Shi, Baoxin; Liu, Yuanhong; Lu, Shixiang; Gao, Zhi-Wei

doi:10.1016/j.conengprac.2024.105885

Control Engineering Practice

2024

DOI: 10.1016/j.conengprac.2024.105885

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A new adaptive feature fusion and selection network for intelligent transportation systems

Baoxin Shi,

Yuanhong Liu,

Shixiang Lu

et al.

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Cited by 1 publication

References 27 publications

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Multimodal and multiscale feature fusion for weakly supervised video anomaly detection

Sun,

Cao,

Guo

et al. 2024

Sci Rep

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Weakly supervised video anomaly detection aims to detect anomalous events with only video-level labels. In the absence of boundary information for anomaly segments, most existing methods rely on multiple instance learning. In these approaches, the predictions for unlabeled video snippets are guided by the classification of labeled untrimmed videos. However, these methods do not account for issues such as video blur and visual occlusion, which can hinder accurate anomaly detection. To address these issues, we propose a novel weakly supervised video anomaly detection method that fuses multimodal and multiscale features. Firstly, RGB and optical flow snippets are input into pre-trained I3D to extract appearance and motion features. Then, we introduce an Attention De-redundancy (AD) module, which employs an attention mechanism to filter out task-irrelevant redundancy in these appearance and motion features. Next, to mitigate the effects of video blurring and visual occlusion, we propose a Multi-scale Feature Learning module. This module captures long-term and short-term temporal dependencies among video snippets to provide global and local guidance for blurred or occluded video snippets. Finally, to effectively utilize the discriminative features of different modalities, we propose an Adaptive Feature Fusion module. This module adaptively fuses appearance and motion features based on their respective feature weights. Extensive experimental results demonstrate that our proposed method outperforms mainstream unsupervised and weakly supervised methods in terms of AUC. Specifically, our proposed method achieves 97.00% AUC and 85.31% AUC on two benchmark datasets, i.e., ShanghaiTech and UCF-Crime, respectively.

show abstract

Multimodal and multiscale feature fusion for weakly supervised video anomaly detection

Sun,

Cao,

Guo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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A new adaptive feature fusion and selection network for intelligent transportation systems

Cited by 1 publication

References 27 publications

Multimodal and multiscale feature fusion for weakly supervised video anomaly detection

Multimodal and multiscale feature fusion for weakly supervised video anomaly detection

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