Real-Time Multi-Human Parsing on Embedded Devices

Multi-human parsing algorithms have significant potential for real-time surveillance applications. By accurately segmenting humans and their body parts, such algorithms help to understand and better differentiate multiple human subjects in video frames. However, deploying such algorithms on resource-constrained embedded devices such as smart cameras presents challenges due to memory constraints and limited computational power. Therefore, this work investigates the limitations of existing multi-human parsing algorithms and proposes MHParsNet, a lightweight yet accurate model for human parsing on embedded devices. Compared to benchmark algorithms, MHParsNet performs competitive segmentation while requiring only 125 MB of memory. We deployed MHParsNet on a smart camera prototype using the Jetson Nano embedded board and achieved an average inference of 6 frames per second. These results demonstrate the effectiveness of MHParsNet and its suitability for real-time applications on resource-constrained embedded devices.