Onboard Person Retrieval System With Model Compression: A Case Study on Nvidia Jetson Orin AGX

Onboard Person Retrieval System With Model Compression: A Case Study on Nvidia Jetson Orin AGX

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A person retrieval system (PRS) in video surveillance identifies an individual based on descriptive attributes, a task that employs several computationally intensive deep learning models.We implement and analyse a PRS for pre-recorded videos on a graphics processing unit (GPU) and Nvidia Jetson Orin AGX.This paper presents a new Person Attribute Recognition (PAR) architecture, CorPAR, using three backbone networks, ConvNext, ResNet-50, and EfficientNet-B0.It enhances the F1-score by 4.1% with ConvNeXT-Base, 1.

63% with the ResNet, read more and by 8.07% with EfficientNet-B0, surpassing the performance of the state-of-the-art Weighted-PAR method.The proposed method uses model compression techniques like quantisation and pruning with L1 regularisation to assess their impact on person retrieval.The study reveals that the PRS utilising EfficientNet-B0, with 32-bit quantisation, achieves the best performance, delivering a throughput of 22 frames per second and a True Positive Rate of 71% on invertatop squeeze bottle Nvidia Jetson Orin AGX matching the performance of a model implemented using GPU.