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Person reidentification (re-ID) is becoming one of the most significantapplication areas of computer vision due to its importance for science andsocial security. Due to enormous size and scale of camera systems it isbeneficial to develop edge computing re-ID applications where at least part ofthe analysis could be performed by the cameras. However, conventional re-IDrelies heavily on deep learning (DL) computationally demanding models which arenot readily applicable for edge computing. In this paper we adapt a recentlyproposed re-ID method that combines DL human parsing with analytical featureextraction and ranking schemes to be more suitable for edge computing re-ID.First, we compare parsers that use ResNet101, ResNet18, MobileNetV2, and OSNetbackbones and show that parsing can be performed using compact backbones withsufficient accuracy. Second, we transfer parsers to tensor processing unit(TPU) of Google Coral Dev Board and show that it can act as a portable edgecomputing re-ID station. We also implement the analytical part of re-ID methodon Coral CPU to ensure that it can perform a complete re-ID cycle. Forquantitative analysis we compare inference speed, parsing masks, and re-IDaccuracy on GPU and Coral TPU depending on parser backbone. We also discusspossible application scenarios of edge computing in re-ID taking into accountknown limitations mainly related to memory and storage space of portabledevices.

Google Coral-based edge computing person reidentification using human parsing combined with analytical method