3 February 2014 Real-time, resource-constrained object classification on a micro-air vehicle
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Abstract
A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.
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Louis Buck, Louis Buck, Laura Ray, Laura Ray, "Real-time, resource-constrained object classification on a micro-air vehicle", Proc. SPIE 9025, Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques, 90250R (3 February 2014); doi: 10.1117/12.2038360; https://doi.org/10.1117/12.2038360
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