Video anomaly detection based on attention and efficient spatio-temporal feature extraction

Rahimpour, Seyed Mohammad; Kazemi, Mohammad; Moallem, Payman; Safayani, Mehran

doi:10.1007/s00371-024-03361-y

Cited by 1 publication

(1 citation statement)

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“…There are many feature information extraction methods in the field of deep learning. Rahimpour et al proposed a method to extract information features from videos using parallel deep structures [26]. The method consists of different units, including an attention unit, frame sampling unit, spatial and temporal feature extractor, and threshold.…”

Section: Related Workmentioning

confidence: 99%

MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images

Wang,

et al. 2024

Preprint

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In hazy weather conditions, a significant accumulation of haze poses a severe challenge to the quality of image capture for autonomous driving systems, thereby heightening safety risks for autonomous vehicles. To tackle this issue, we propose the Multi-scale Refinement Dehazing Network (MRD-Net), an innovative architecture comprising a front-end module, a backbone module, and a tail-end module, specifically designed to eradicate haze with precision. To enhance the extraction of multi-scale features, the backbone module employs the Squeeze-Excitation Residual Dense Block (SRD). It not only learns the intricate multi-scale features of the image but also adaptively recalibrates the feature response of each feature map, ultimately bolstering the network's performance and resilience. The tail-end module, crafted with the Dilation Refinement Block (DRB), serves as a compensatory measure for any detail loss or pseudo-artifacts that might arise from the backbone module's operations. By incorporating this refinement block, the overall dehazing effect is further optimized. Empirical evaluations reveal that the proposed MRD-Net achieves impressive results, with a PSNR value of 28.12, an SSIM value of 0.964, and an LPIPS value of 0.032. These figures indicate that the network is adept at removing haze from images while preserving intricate details, ensuring the efficacy and reliability of autonomous driving systems in hazy environments.

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Section: Related Workmentioning

confidence: 99%

MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images

Wang,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Video anomaly detection based on attention and efficient spatio-temporal feature extraction

Cited by 1 publication

References 41 publications

MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images

MRD-Net: Multi-scale Refinement Dehazing Network for Autonomous Driving Perception Images

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