Goal and Elite-Data-Driven Anthropomorphic Learning for Streaming-Image Analytics

With the rise of the Internet of Things (IoT), many edge cameras (smart cameras that leverage edge computing) have provided real-time streaming-image analytics using anthropomorphic learning. However, previous anthropomorphic-learning studies did not allow the setting of customized goals to filter out concerned features during online model learning. In addition, when any concept drift occurs, the existing studies using an edge camera cannot adapt efficiently using only representative images. Our study therefore proposes solutions to the above issues based on the self-regulated learning framework, which divides a learning procedure into before, during, and after stages to improve overall learning performance, just like human beings. In the before learning stage of a model on an edge camera, we propose a generative adversarial network (GAN)-based customized feature filtering to screen out users’ concerned features before real-time model learning of image analytics. Next, during the learning stage of the model, we also propose keyframe-oriented model adaptation that can effectively respond to concept drift while reducing image storage space on an edge camera. The experimental results show that customized concerned feature filtering can improve the adversarial effect of the concerned features by 24.26 percent on average, meaning it can avoid using unwanted features from learning an undesirable model. The keyframe-oriented model adaptation can accelerate the overall adjustment time by 52 percent and save the required storage space by 54.7 percent at the cost of compromising precision by 2 percent at most.