  Neuromorphic vision-based sensors are gaining popularity in recent years with
their ability to capture Spatio-temporal events with low power sensing. These
sensors record events or spikes over traditional cameras which helps in
preserving the privacy of the subject being recorded. These events are captured
as per-pixel brightness changes and the output data stream is encoded with
time, location, and pixel intensity change information. This paper proposes and
benchmarks the performance of fine-tuned conventional vision models on
neuromorphic human action recognition and fall detection datasets. The
Spatio-temporal event streams from the Dynamic Vision Sensing cameras are
encoded into a standard sequence image frames. These video frames are used for
benchmarking conventional deep learning-based architectures. In this proposed
approach, we fine-tuned the state-of-the-art vision models for this Dynamic
Vision Sensing (DVS) application and named these models as DVS-R2+1D, DVS-CSN,
DVS-C2D, DVS-SlowFast, DVS-X3D, and DVS-MViT. Upon comparing the performance of
these models, we see the current state-of-the-art MViT based architecture
DVS-MViT outperforms all the other models with an accuracy of 0.958 and an F-1
score of 0.958. The second best is the DVS-C2D with an accuracy of 0.916 and an
F-1 score of 0.916. Third and Fourth are DVS-R2+1D and DVS-SlowFast with an
accuracy of 0.875 and 0.833 and F-1 score of 0.875 and 0.861 respectively.
DVS-CSN and DVS-X3D were the least performing models with an accuracy of 0.708
and 0.625 and an F1 score of 0.722 and 0.625 respectively.
