  Efficient video processing is a critical component in many IoMT applications
to detect events of interest. Presently, many window optimization techniques
have been proposed in event processing with an underlying assumption that the
incoming stream has a structured data model. Videos are highly complex due to
the lack of any underlying structured data model. Video stream sources such as
CCTV cameras and smartphones are resource-constrained edge nodes. At the same
time, video content extraction is expensive and requires computationally
intensive Deep Neural Network (DNN) models that are primarily deployed at
high-end (or cloud) nodes. This paper presents VID-WIN, an adaptive 2-stage
allied windowing approach to accelerate video event analytics in an edge-cloud
paradigm. VID-WIN runs parallelly across edge and cloud nodes and performs the
query and resource-aware optimization for state-based complex event matching.
VID-WIN exploits the video content and DNN input knobs to accelerate the video
inference process across nodes. The paper proposes a novel content-driven
micro-batch resizing, queryaware caching and micro-batch based utility
filtering strategy of video frames under resource-constrained edge nodes to
improve the overall system throughput, latency, and network usage. Extensive
evaluations are performed over five real-world datasets. The experimental
results show that VID-WIN video event matching achieves ~2.3X higher throughput
with minimal latency and ~99% bandwidth reduction compared to other baselines
while maintaining query-level accuracy and resource bounds.
