High-speed object matching and localization using gradient orientation features

In many robotics and automation applications, it is often required to detect a given object and determine its pose (position and orientation) from input images with high speed, high robustness to photometric changes, and high pose accuracy. We propose a new object matching method that improves efficiency over existing approaches by decomposing orientation and position estimation into two cascade steps. In the first step, an initial position and orientation is found by matching with Histogram of Oriented Gradients (HOG), reducing orientation search from 2D template matching to 1D correlation matching. In the second step, a more precise orientation and position is computed by matching based on Dominant Orientation Template (DOT), using robust edge orientation features. The cascade combination of the HOG and DOT feature for high-speed and robust object matching is the key novelty of the proposed method. Experimental evaluation was performed with real-world single-object and multi-object inspection datasets, using software implementations on an Atom CPU platform. Our results show that the proposed method achieves significant speed improvement compared to an already accelerated template matching method at comparable accuracy performance.