  Despite the continued successes of computationally efficient deep neural
network architectures for video object detection, performance continually
arrives at the great trilemma of speed versus accuracy versus computational
resources (pick two). Current attempts to exploit temporal information in video
data to overcome this trilemma are bottlenecked by the state-of-the-art in
object detection models. We present, a technique which performs video object
detection through the use of off-the-shelf object detectors alongside existing
optical flow based motion estimation techniques in parallel. Through a set of
experiments on the benchmark MOT20 dataset, we demonstrate that our approach
significantly reduces the baseline latency of any given object detector without
sacrificing any accuracy. Further latency reduction, up to 25x lower than the
original latency, can be achieved with minimal accuracy loss. MOVEX enables low
latency video object detection on common CPU based systems, thus allowing for
high performance video object detection beyond the domain of GPU computing. The
code is available at https://github.com/juliantrue/movex.
