Faster Robot Perception Using Salient Depth Partitioning

Chan, D., Taylor, A & Riek, L.D., Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017).
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This paper introduces Salient Depth Partitioning (SDP), a depth-based region cropping algorithm devised to be easily adapted to existing detection algorithms. SDP is designed to give robots a better sense of visual attention, and to reduce the processing time of pedestrian detectors. In contrast to proposal generators, our algorithm generates sparse regions, to combat image degradation caused by robot motion, making them more suitable for real-world operation. Furthermore, SDP is able achieve real-time performance (77 frames per second) on a single processor without a GPU. Our algorithm requires no training, and is designed to work with any pedestrian detection algorithm, provided that the input is in the form of a calibrated RGB-D image. We tested our algorithm with four state-of-the-art pedestrian detectors (HOG and SVM [1], Aggregate Channel Features [2], Checkerboards [3], and R-CNN [4]), and show that it improves computation time by up to 30%, with no discernible change in accuracy.