Real time algorithms for human versus animal classification using a pyroelectric sensor


Classification of human and animal targets imaged by a linear pyroelectic array senor presents some unique challenges especially in target segmentation and feature extraction. In this paper, we apply two approaches to address this problem. Both techniques start with the variational energy functional level set segmentation technique to separate the object from background. After segmentation, in the first technique, we extract features such as texture, invariant moments, edge, shape information, and spectral contents of the segmented object. These features are fed to classifiers including Naïve Bayesian (NB), and Support Vector Machine (SVM) for human against animal classification. In the second technique, the speeded up robust feature (SURF) extraction algorithm is applied to the segmented objects. A code book technique is used to classify objects based on SURF features. Human and animal data acquired-using the pyroelectric sensor in different terrains, are used for performance evaluation of the algorithms. The evaluation indicates that the features extracted in the first technique in conjunction with the NB classifier provide the highest classification rates. While the SURF feature plus code book approach provides a slightly lower classification rate, it provides better computational efficiency lending itself to real time implementation. © 2013 SPIE.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering