Toward Always-On Mobile Object Detection: Energy Versus Performance Tradeoffs for Embedded HOG Feature Extraction

This paper studies the effects of front-end imager parameters on object detection performance and energy consumption. A custom version of histograms of oriented gradient (HOG) features based on 2-b pixel ratios is presented and shown to achieve superior object detection performance for the same estimated energy compared with conventional HOG features. A front-end hardware implementation capable of extracting these features at multiple scales is proposed, and a system-level energy analysis is performed. This energy analysis suggests a potential $19\times $ reduction in I/O energy and a $3.3\times $ reduction in back-end detection energy compared with conventional object detection pipelines.