LAUSR

Wireless image sensor nodes are required to deliver better visual information of the region-of-interest (ROI) under tight energy constraints. The energy-quality scalability of the sensor node can be improved by incorporating ROI-based image processing. This paper presents an energy-quality scalable wireless image sensor node using ROI-based processing for object-based surveillance. After detecting the ROI using a low-power noise-robust method, frame images are encoded by a low-complexity ROI coding scheme with better rate-quality performance. The ROI-based processing parameters are controlled by a simple on-line rate controller to minimize the buffer requirement. We integrate the proposed approach into the wireless image sensor node, and analyze the performance assuming a base station with human operators and deep neural networks. The results show that the proposed approach provides higher quality under the available energy budget. Better energy-quality scalability yields higher energy-accuracy performance for the intelligent surveillance system with deep neural networks.