  Edge computing is being widely used for video analytics. To alleviate the
inherent tension between accuracy and cost, various video analytics pipelines
have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we
find that GPU compute resources provisioned for edge nodes are commonly
under-utilized due to video content variations, subsampling and filtering at
different places of a pipeline. As opposed to model and pipeline optimization,
in this work, we study the problem of opportunistic data enhancement using the
non-deterministic and fragmented idle GPU resources. In specific, we propose a
task-specific discrimination and enhancement module and a model-aware
adversarial training mechanism, providing a way to identify and transform
low-quality images that are specific to a video pipeline in an accurate and
efficient manner. A multi-exit model structure and a resource-aware scheduler
is further developed to make online enhancement decisions and fine-grained
inference execution under latency and GPU resource constraints. Experiments
across multiple video analytics pipelines and datasets reveal that by
judiciously allocating a small amount of idle resources on frames that tend to
yield greater marginal benefits from enhancement, our system boosts DNN object
detection accuracy by $7.3-11.3\%$ without incurring any latency costs.
