Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos

Hamdi, Slim; Bouindour, Samir; Loukil, Kais; Snoussi, Hichem; Abid, Mohamed

doi:10.5220/0008949405140521

Cited by 1 publication

(2 citation statements)

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“…The decoder, on the other hand, translates the internal representation back to the original reconstructed image. Autoencoder provides a reconstruction error [19]. The minimum reconstruction error means a slight difference between the input and the reconstructed image frames [20].…”

Section: Convolutional Autoencodermentioning

confidence: 99%

“…At ground level, liquid water clouds tend to reflect incoming sunlight, which helps chill the surface [18]. Additionally, clouds in the upper troposphere that comprise ice are capable of absorbing heat from the surface and reemitting it, therefore contributing to the rise in surface temperature [19]. Dust, smoke, sulfur, and particles of fossil fuel burning are all examples of aerosol particles.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

et al. 2022

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Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary to understand the dynamics of climate forcing and enhance our knowledge of climate change. Because of this reactivity to precipitation, temperature, topography, and human activity, the atmospheric boundary layer (ABL) is one of the most dynamic atmospheric regions: ABL aerosols have a big impact on the evolution of climate change’s radiative forcing, human health, food security, and, eventually, the local and global economy. Continuous monitoring and instrumental and computational approaches are required for the detection and analysis of ABL pattern behavior. This paper provides a deep learning-based outer layer aerosol detection system based on Light Detection and Ranging (LiDAR) data fusion. The suggested method applies sequential models to turn low-level data into compressed features using object-based analysis, feature-level fusion, and autoencoder-based dimensionality reduction. Convolutional neural networks (CNNs) were used to convert compressed data into high-level properties that could be used to categorize air particles in the outer layer. This research describes deep learning approaches that allowed for detecting 40% more atmospheric features at a horizontal resolution of 5 km during daytime operations when applied to LiDAR data. Compared to existing deep learning algorithms for edges and complicated near-surface sceneries during the day, a convolutional autoencoder (CAE) trained using LiDAR dataset standard data products showed the potential for improved aerosol discrimination with 98% accuracy.

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Section: Convolutional Autoencodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%