Investigation of MobileNet-Ssd on human follower robot for stand-alone object detection and tracking using Raspberry Pi

Human following is a very useful task in the robotics industry. With modern compact-sized robots, there is a demand for further investigated computer-vision solutions that can perform effectively on them. A well-known deep learning model along this line of thought is the MobileNet-Ssd, an object detection model renowned for its resource-constrained usage. Available in popular frameworks like TensorFlow and PyTorch, this model can be of great use in deployments on robotic applications. This research attempts to investigate the MobileNet-Ssd model in order to evaluate its suitability for stand-alone object detection on a Raspberry Pi. To determine the effect of input size on the model, the model’s performance has been investigated with speed in frames-per-second across different input sizes on both CPU and GPU-powered devices. To evaluate the model’s effectiveness in the human following task, a Raspberry Pi-based robot was designed leveraging the tracking-by-detection approach with TensorFlow-Lite. Furthermore, the model’s performance was evaluated using PyTorch while the model’s inputs were adjusted, and the results were compared to those of other state-of-the-art models. The investigation revealed that, despite its modest speeds, the model outperforms other noteworthy models in PyTorch and is an ideal choice when working with Raspberry Pi using TensorFlow-Lite.