Resource-efficient Text-based Person Re-identification on Embedded Devices

This work addresses the challenge of textbased person re-identification (re-ID) on resource-constrained embedded devices, a critical component in modern surveillance systems. Text-based person re-ID involves using textual descriptions to search persons across multiple camera views. Implementing such algorithms on embedded devices such as smart cameras is challenging due to limited memory and computational constraints. In this work, we propose TextReIDNet, a lightweight person re-ID model designed explicitly for embedded devices. Compared to state-of-the-art models, TextReIDNet aims at an optimal balance between person re-ID accuracy and computational efficiency, thus making it well-suited for low-resource devices. With the smallest model size of only 32.29 million parameters, TextReIDNet achieves a competitive 52.76% and 35.71% top-1 accuracy on the CUHK-PEDES and RSTPReid datasets, respectively. We implemented TextReIDNet on the Jetson Nano board to demonstrate its capability for embedded deployments. On average, TextReIDNet requires 1.13 ms to process a text and 30.92 ms for an image.