  The recognition of behaviors in videos usually requires a combinatorial
analysis of the spatial information about objects and their dynamic action
information in the temporal dimension. Specifically, behavior recognition may
even rely more on the modeling of temporal information containing short-range
and long-range motions; this contrasts with computer vision tasks involving
images that focus on the understanding of spatial information. However, current
solutions fail to jointly and comprehensively analyze short-range motion
between adjacent frames and long-range temporal aggregations at large scales in
videos. In this paper, we propose a novel behavior recognition method based on
the integration of multigranular (IMG) motion features. In particular, we
achieve reliable motion information modeling through the synergy of a channel
attention-based short-term motion feature enhancement module (CMEM) and a
cascaded long-term motion feature integration module (CLIM). We evaluate our
model on several action recognition benchmarks such as HMDB51,
Something-Something and UCF101. The experimental results demonstrate that our
approach outperforms the previous state-of-the-art methods, which confirms its
effectiveness and efficiency.
