  It is a common practice to think of a video as a sequence of images (frames),
and re-use deep neural network models that are trained only on images for
similar analytics tasks on videos. In this paper, we show that this leap of
faith that deep learning models that work well on images will also work well on
videos is actually flawed. We show that even when a video camera is viewing a
scene that is not changing in any human-perceptible way, and we control for
external factors like video compression and environment (lighting), the
accuracy of video analytics application fluctuates noticeably. These
fluctuations occur because successive frames produced by the video camera may
look similar visually, but these frames are perceived quite differently by the
video analytics applications. We observed that the root cause for these
fluctuations is the dynamic camera parameter changes that a video camera
automatically makes in order to capture and produce a visually pleasing video.
The camera inadvertently acts as an unintentional adversary because these
slight changes in the image pixel values in consecutive frames, as we show,
have a noticeably adverse impact on the accuracy of insights from video
analytics tasks that re-use image-trained deep learning models. To address this
inadvertent adversarial effect from the camera, we explore the use of transfer
learning techniques to improve learning in video analytics tasks through the
transfer of knowledge from learning on image analytics tasks. In particular, we
show that our newly trained Yolov5 model reduces fluctuation in object
detection across frames, which leads to better tracking of objects(40% fewer
mistakes in tracking). Our paper also provides new directions and techniques to
mitigate the camera's adversarial effect on deep learning models used for video
analytics applications.
