  Performing analytics tasks over large-scale video datasets is increasingly
common in a wide range of applications. These tasks generally involve object
detection and tracking operations that require applying expensive machine
learning models, and several systems have recently been proposed to optimize
the execution of video queries to reduce their cost. However, prior work
generally optimizes execution speed in only one dimension, focusing on one
optimization technique while ignoring other potential avenues for accelerating
execution, thereby delivering an unsatisfactory tradeoff between speed and
accuracy. We propose MultiScope, a general-purpose video pre-processor for
object detection and tracking that explores multiple avenues for optimizing
video queries to extract tracks from video with a superior tradeoff between
speed and accuracy over prior work. We compare MultiScope against three recent
systems on seven diverse datasets, and find that it provides a 2.9x average
speedup over the next best baseline at the same accuracy level.
