Teacher-student models for AI vision at the edge: a car parking case study

The surge of the Internet of Things has sparked a multitude of deep learning-based computer vision applications that extract relevant information from the deluge of data coming from Edge devices, such as smart cameras. Nevertheless, this promising approach introduces new obstacles, including the constraints posed by the limited computational resources on these devices and the challenges associated with the generalization capabilities of the AI-based models against novel scenarios never seen during the supervised training, a situation frequently encountered in this context. This work proposes an efficient approach for detecting vehicles in parking lot scenarios monitored by multiple smart cameras that train their underlying AI-based models by exploiting knowledge distillation. Specifically, we consider an architectural scheme comprising a powerful and large detector used as a teacher and several shallow models acting as students, more appropriate for computational-bounded devices and designed to run onboard the smart cameras. The teacher is pre-trained over general-context data and behaves like an oracle, transferring its knowledge to the smaller nodes; on the other hand, the students learn to localize cars in new specific scenarios without using further labeled data, relying solely on the distilled loss coming from the oracle. Preliminary results show that student models trained only with distillation loss increase their performances, sometimes even outperforming the results achieved by the same models supervised with the ground truth.