  Cameras are increasingly being deployed in cities, enterprises and roads
world-wide to enable many applications in public safety, intelligent
transportation, retail, healthcare and manufacturing. Often, after initial
deployment of the cameras, the environmental conditions and the scenes around
these cameras change, and our experiments show that these changes can adversely
impact the accuracy of insights from video analytics. This is because the
camera parameter settings, though optimal at deployment time, are not the best
settings for good-quality video capture as the environmental conditions and
scenes around a camera change during operation. Capturing poor-quality video
adversely affects the accuracy of analytics. To mitigate the loss in accuracy
of insights, we propose a novel, reinforcement-learning based system APT that
dynamically, and remotely (over 5G networks), tunes the camera parameters, to
ensure a high-quality video capture, which mitigates any loss in accuracy of
video analytics. As a result, such tuning restores the accuracy of insights
when environmental conditions or scene content change. APT uses reinforcement
learning, with no-reference perceptual quality estimation as the reward
function. We conducted extensive real-world experiments, where we
simultaneously deployed two cameras side-by-side overlooking an enterprise
parking lot (one camera only has manufacturer-suggested default setting, while
the other camera is dynamically tuned by APT during operation). Our experiments
demonstrated that due to dynamic tuning by APT, the analytics insights are
consistently better at all times of the day: the accuracy of object detection
video analytics application was improved on average by ~ 42%. Since our reward
function is independent of any analytics task, APT can be readily used for
different video analytics tasks.
