TL;DR
This work proposes a novel multi-modal heat conduction operation framework for effective activity recognition, termed MMHCO-HAR, and conducts comprehensive experiments comparing the proposed method with baseline methods on the HARDVS 2.0 dataset and other public datasets.
The article compares the performance of traditional machine learning techniques with recent deep learning architectures such as CNNs, RNNs, TCNs, and Transformers, based on accuracy, computational cost, and suitability for real-world disorderly plotting.
This paper introduces EventKitchen, a large-scale stereo event camera benchmark dataset of human cooking activities in the kitchen that establishes a challenging benchmark for neuromorphic vision beyond autonomous driving.
The findings indicate that while event cameras do not yet surpass strong RGB transformer pipelines, they constitute an efficient and complementary modality for violence understanding.
This work introduces Emo-DVS, the first large-scale event-based emotion analysis dataset, which couples dynamic illumination with the Facial Action Unit (FAU) subset and emotion subset and proposes the Information-Guided Gated Fusion (IGF) framework, which achieves state-of-the-art performance.
Human Activity Recognition (HAR) is a fast-growing research area that focuses on identifying human actions using data collected from sensors and vision-based devices. It plays an important role in applications like health monitoring, smart homes, surveillance, sports analysis, and human-computer interaction. In recent...
Human action recognition (HAR) plays a crucial role in safety monitoring, intelligent surveillance systems, and human-computer interaction applications. In this study, we evaluate and compare several deep learning architectures for HAR using the Weizmann dataset, using a YOLO-based preprocessing, including CNN, CNN wit...
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