  Neuromorphic sensors, specifically event cameras, revolutionize visual data
acquisition by capturing pixel intensity changes with exceptional dynamic
range, minimal latency, and energy efficiency, setting them apart from
conventional frame-based cameras. The distinctive capabilities of event cameras
have ignited significant interest in the domain of event-based action
recognition, recognizing their vast potential for advancement. However, the
development in this field is currently slowed by the lack of comprehensive,
large-scale datasets, which are critical for developing robust recognition
frameworks. To bridge this gap, we introduces DailyDVS-200, a meticulously
curated benchmark dataset tailored for the event-based action recognition
community. DailyDVS-200 is extensive, covering 200 action categories across
real-world scenarios, recorded by 47 participants, and comprises more than
22,000 event sequences. This dataset is designed to reflect a broad spectrum of
action types, scene complexities, and data acquisition diversity. Each sequence
in the dataset is annotated with 14 attributes, ensuring a detailed
characterization of the recorded actions. Moreover, DailyDVS-200 is structured
to facilitate a wide range of research paths, offering a solid foundation for
both validating existing approaches and inspiring novel methodologies. By
setting a new benchmark in the field, we challenge the current limitations of
neuromorphic data processing and invite a surge of new approaches in
event-based action recognition techniques, which paves the way for future
explorations in neuromorphic computing and beyond. The dataset and source code
are available at https://github.com/QiWang233/DailyDVS-200.
