Fast Enhanced DWT based Video Micro Movement Magnification

Fahmy, Gamal; Fahmy, Omar M.; Fahmy, Mahmoud

doi:10.1109/isspit47144.2019.9001874

Cited by 3 publications

(1 citation statement)

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“…Wavelet transform is one of the important applications in digital image processing. There have been several approaches performed by the authors that used different types of wavelet transforms with different filter designs for multiple applications as in [2,3]. Lately, a lot of methodologies that combined wavelets with Deep learning as in [4][5][6][7][8] were proposed which made such approaches to be used in many applications as in [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

MultiWave-Net: An Optimized Spatiotemporal Network for Abnormal Action Recognition Using Wavelet-Based Channel Augmentation

Elmasry,

Abd El Ghany,

Salem

et al. 2024

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Human behavior is regarded as one of the most complex notions present nowadays, due to the large magnitude of possibilities. These behaviors and actions can be distinguished as normal and abnormal. However, abnormal behavior is a vast spectrum, so in this work, abnormal behavior is regarded as human aggression or in another context when car accidents occur on the road. As this behavior can negatively affect the surrounding traffic participants, such as vehicles and other pedestrians, it is crucial to monitor such behavior. Given the current prevalent spread of cameras everywhere with different types, they can be used to classify and monitor such behavior. Accordingly, this work proposes a new optimized model based on a novel integrated wavelet-based channel augmentation unit for classifying human behavior in various scenes, having a total number of trainable parameters of 5.3 m with an average inference time of 0.09 s. The model has been trained and evaluated on four public datasets: Real Live Violence Situations (RLVS), Highway Incident Detection (HWID), Movie Fights, and Hockey Fights. The proposed technique achieved accuracies in the range of 92% to 99.5% across the used benchmark datasets. Comprehensive analysis and comparisons between different versions of the model and the state-of-the-art have been performed to confirm the model’s performance in terms of accuracy and efficiency. The proposed model has higher accuracy with an average of 4.97%, and higher efficiency by reducing the number of parameters by around 139.1 m compared to other models trained and tested on the same benchmark datasets.

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