DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition
<|reference_start|>DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition: 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.<|reference_end|>
@article{wang2024dailydvs-200:,
    title={DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action
  Recognition},
    author={Qi Wang, Zhou Xu, Yuming Lin, Jingtao Ye, Hongsheng Li, Guangming Zhu,
  Syed Afaq Ali Shah, Mohammed Bennamoun, Liang Zhang},
    journal={arXiv preprint arXiv:2407.05106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05106},
    primaryClass={cs.CV}
}