Low-Cost IoT Surveillance System Using Hardware-Acceleration and Convolutional Neural Networks
Epaminondas De Souza Lage, Rodolfo L. Santos, Sandro M.T., Fernando Andreotti · 2019
Reliable object detection is crucial for intelligent surveillance systems. Despite the abundance of available systems, these are often expensive or require cloud connectivity which restricts their usage. In this paper, we present a complete low-cost solution using embedded devices. Person detection is performed locally, using an efficient implementation of a state-of-the-art convolutional Single Shot Detector. The proposed model is fine-tuned to the surveillance task at hand and tested using a Raspberry Pi and the Up Squared Board devices. Additionally, the benefits of hardware-acceleration are evaluated using an Intel®Movidius™ Neural Compute Stick (NCS). The final model performs person detection with an average precision of 0.48. The Raspberry Pi is capable of acquiring and processing images at a rate of 0.4 to 5.3 FPS without/with NCS, whereas the Up Board achieves 2.1 to 7.3 FPS. Further tests demonstrate how the system performs when multiple cameras and multiple NCS devices are deployed. In conclusion, the proposed architecture is suitable for real-time, affordable surveillance systems in home and commercial settings.