IoT-Enabled Streaming Image Analytics With Privacy-Aware Self-Adaptive and Reflective Designs

With the development of the Internet of Things (IoT), a large number of cameras are able to generate large amounts of real-time data. This has attracted increasing attention to streaming-image analytics incorporating edge computing. Since streaming images do not have ground-truth labels, their outputs may become untrustworthy once any concept drift or uncertainty occurs. To keep analytics models updated in response to concept drift, much human intervention is required, particularly in system maintenance for IoT-enabled applications with a plethora of smart cameras. To address this issue and follow the trend of anthropomorphic machine learning, this article incorporates self-regulated learning from human pedagogy, which comprises forethought, performance, and reflection. Our article proposes counterparts for streaming-image analytics, including siaForethought, siaPerformance, and siaReflection, respectively. The experiment results show that siaForethought can effectively blur the detected pedestrians or the whole image to improve privacy awareness. Next, siaPerformance can help reduce the model adaptation time by 38%, even when using blurred pedestrians or images. Finally, siaReflection reduces about 50% of human intervention for labeling data, thus greatly reducing human interventions in case of concept drift.