In this paper, we propose a real-time human versus animal classification technique using a pyro-electric sensor array and Hidden Markov Model. The technique starts with the variational energy functional level set segmentation technique to separate the object from background. After segmentation, we convert the segmented object to a signal by considering column-wise pixel values and then finding the wavelet coefficients of the signal. HMMs are trained to statistically model the wavelet features of individuals through an expectation-maximization learning process. Human versus animal classifications are made by evaluating a set of new wavelet feature data against the trained HMMs using the maximum-likelihood criterion. Human and animal data acquired-using a pyro-electric sensor in different terrains are used for performance evaluation of the algorithms. Failures of the computationally effective SURF feature based approach that we develop in our previous research are because of distorted images produced when the object runs very fast or if the temperature difference between target and background is not sufficient to accurately profile the object. We show that wavelet based HMMs work well for handling some of the distorted profiles in the data set. Further, HMM achieves improved classification rate over the SURF algorithm with almost the same computational time.

Date Published: 5 March 2014
PDF: 9 pages
Proc. SPIE 9026, Video Surveillance and Transportation Imaging Applications 2014, 90260J (5 March 2014); doi: 10.1117/12.2040537

Published in SPIE Proceedings Vol. 9026:
Video Surveillance and Transportation Imaging Applications 2014
Robert P. Loce; Eli Saber; Ned Lecky, Editor(s)