Hyperbolic function based hybrid consistent adaptive sequential DL algorithms for PIR sensor based intruder detection

Abstract

This study demonstrates that a novel dictionary learning (DL) algorithm-based approach can be successfully used in conjunction with an indigenously developed hardware module, using four pyroelectric infrared (PIR) sensors, to enhance the performance of an intruder detection system. In this work, initially, a hyperbolic function based Consistent Adaptive Sequential Dictionary Learning (CAS-DL) algorithm has been proposed (HCAS-DL). Then another dictionary learning algorithm, called label consistency based K-SVD (LCK-SVD) is considered, where the label information of each dictionary atom has been introduced to enforce discriminability in sparse code. The objective function in LCK-SVD is formuated simultaneously considering the concept of label consistency (LC) constraints, reconstruction errors, and classification errors. Next, the newly proposed HCAS-DL is hybridized with LCK-SVD to form a novel version of hybrid consistent adaptive sequential dictionary learning approach, named here as the LC-HCAS-DL algorithm. Extensive experiments have been performed to establish the suitability of our proposed approach for the problem under consideration. The performance evaluation clearly establishes that the LC-HCAS-DL algorithm can achieve superior performance compared to recently proposed LC-MCAS-DL algorithm and other state-of-the-art algorithms for the intruder detection problem under consideration.