6 July 2015 Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model
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Abstract
Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation–maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jakir Hossen, Jakir Hossen, Eddie Jacobs, Eddie Jacobs, Srikant Chari, Srikant Chari, } "Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model," Optical Engineering 54(7), 073102 (6 July 2015). https://doi.org/10.1117/1.OE.54.7.073102 . Submission:
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