Little-Big Deep Neural Networks for Embedded Video Surveillance
Catalin Alexandru Mitrea, Mihai Gabriel Constantin, Liviu–Daniel Stefan, Marian Traian Ghenescu, Bogdan Emanuel Ionescu · 2018 International Conference on Communications (COMM) · 2018
Embedded systems are under continuous development of innovative technological trends, such as adoption of smart devices which are becoming capable of running complex video analytics tasks locally. Lately, deep neural networks have been successfully applied in the field of computer vision achieving state-of-the-art results. These techniques are not yet suitable for resource limited deployments due to high memory footprint and computational cost, factors that affect the inference time. To tackle these constraints, we propose a person re-identification architecture based on the DarkNET Deep Learning Neural Network architecture for person detection and segmentation, which is combined with SIFT algorithm for feature extraction and SVM for the classification task. The algorithm is implemented on a low processing embedded hardware system, namely Raspberry PI. The experiments were conducted on the proposed SPOTTER dataset. The results are conclusive to continue further investigation of applying specialized algorithms for real-time applications which can run on resource limited embedded systems.