Visual Computing and Understanding: New Developments and Trends

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Computers computers	2.6	5.4	2012	17.2 Days	CHF 1800	Submit
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Information information	2.4	6.9	2010	14.9 Days	CHF 1600	Submit
Journal of Imaging jimaging	2.7	5.9	2015	20.9 Days	CHF 1800	Submit

Abstract

Video anomaly detection (VAD) plays a crucial role in fields such as security, production, and transportation. To address the issue of overgeneralization in anomaly behavior prediction by deep neural networks, we propose a network called AMFCFBMem-Net (appearance and motion feature cross-fusion block memory network), which combines appearance and motion feature cross-fusion blocks. Firstly, dual encoders for appearance and motion are employed to separately extract these features, which are then integrated into the skip connection layer to mitigate the model’s tendency to predict abnormal behavior, ultimately enhancing the prediction accuracy for abnormal samples. Secondly, a motion foreground extraction module is integrated into the network to generate a foreground mask map based on speed differences, thereby widening the prediction error margin between normal and abnormal behaviors. To capture the latent features of various models for normal samples, a memory module is introduced at the bottleneck of the encoder and decoder structures. This further enhances the model’s anomaly detection capabilities and diminishes its predictive generalization towards abnormal samples. The experimental results on the UCSD Pedestrian dataset 2 (UCSD Ped2) and CUHK Avenue anomaly detection dataset (CUHK Avenue) demonstrate that, compared to current cutting-edge video anomaly detection algorithms, our proposed method achieves frame-level AUCs of 97.5% and 88.8%, respectively, effectively enhancing anomaly detection capabilities. Full article

17 pages, 6653 KiB

Open AccessArticle

Supervised-Learning-Based Method for Restoring Subsurface Shallow-Layer Q Factor Distribution

by Danfeng Zang, Jian Li, Chuankun Li, Mingxing Ma, Chenli Guo and Jiangang Wang

Electronics 2024, 13(11), 2145; https://doi.org/10.3390/electronics13112145 - 30 May 2024

Viewed by 422

Abstract

The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing the integrity of subsurface structures and serves as a primary parameter for evaluating the attenuation characteristics of seismic waves propagating through subsurface media. As the complexity of underground spaces increases, regions expand, and testing environments diversify, the survivability of test nodes is compromised, resulting in sparse effective seismic data with a low signal-to-noise ratio (SNR). Within the confined area defined by the source and sensor placement, only the Q factor along the wave propagation path can be estimated with relative accuracy. Estimating the Q factor in other parts of the area is challenging. Additionally, in recent years, deep neural networks have been employed to address the issue of missing values in seismic data; however, these methods typically require large datasets to train networks that can effectively fit the data, making them less applicable to our specific problem. In response to this challenge, we have developed a supervised learning method for the restoration of shallow subsurface Q factor distributions. The process begins with the construction of an incomplete labeled data volume, followed by the application of a block-based data augmentation technique to enrich the training samples and train the network. The uniformly partitioned initial data are then fed into the trained network to obtain output data, which are subsequently combined to form a complete Q factor data volume. We have validated this training approach using various networks, all yielding favorable results. Additionally, we compared our method with a data augmentation approach that involves creating random masks, demonstrating that our method reduces the mean absolute percentage error (MAPE) by 5%. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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14 pages, 5118 KiB

Open AccessArticle

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

by Zengyun Liu, Zexun Zheng, Tianyi Qin, Liying Xu and Xu Zhang

Electronics 2024, 13(5), 982; https://doi.org/10.3390/electronics13050982 - 4 Mar 2024

Viewed by 926

Abstract

With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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