Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level
intensity changes in the form of" events". Due to their sensing mechanism, event cameras …

Traditional frame-based cameras, despite their effectiveness and usage in computer vision,
exhibit limitations such as high latency, low dynamic range, high power consumption, and …

Hand gestures are a well-known and intuitive method of human-computer interaction. The
majority of the research has concentrated on hand gesture recognition from the RGB …

Contactless RF-based sensing techniques are emerging as a viable means for building
gesture recognition systems. While promising, existing RF-based gesture solutions have …

The neuromorphic event cameras can efficiently sense the latent geometric structures and
motion clues of a scene by generating asynchronous and sparse event signals. Due to the …

Human Pose Estimation (HPE) based on RGB images has experienced a rapid
development benefiting from deep learning. However, event-based HPE has not been fully …

Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …

The main streams of human activity recognition (HAR) algorithms are developed based on
RGB cameras which usually suffer from illumination, fast motion, privacy preservation, and …

Dynamic action recognition has promising applications in human–computer interaction,
information encryption, and high-speed image processing. However, it is challenging for a …