Low-cost Pyroelectric Sensor Networks for Bayesian Crowded Scene Analysis

In this paper, we present a framework for complex scenarios recognition with crowded walkers. This study aims to develop an alternative surveillance system to traditional video camera and visual sensor based systems. Instead of utilizing visual devices in traditional surveillance systems, our crowded scene analysis is based on PIR (Pyroelectric Infrared) sensor networks with intelligent algorithms for context pattern extraction and analysis. Specifically, we will propose two new ideas to handle the crowded scenes: (1) Use hierarchical Bayesian NMF (Non-negative Matrix Factorization) algorithm to automatically identify the basic pattern basis, which will be used for accurate scenario recognition; (2) Use a tree based structure to organize all basic features for fast object recognition. The experimental results valid the efficiency of the proposed two schemes on crowded scenario recognition with low-cost, non-visual system. The results also demonstrate that our framework is appropriate to be implemented in a wireless sensor based monitoring system under severe circumstances.