A vehicle occupant counting system based on near-infrared phenomenology and fuzzy neural classification

Abstract

We undertook a study to determine if the automatic detection and counting of vehicle occupants is feasible. An automated vehicle occupant counting system would greatly facilitate the operation of freeway lanes reserved for buses, car-pools, and emergency vehicles (HOV lanes). In the present paper, we report our findings regarding the appropriate sensor phenomenology and arrangement for the task. We propose a novel system based on fusion of near-infrared imaging signals and we demonstrate its adequacy with theoretical and experimental arguments. We also propose a fuzzy neural network classifier to operate upon the fused near-infrared imagery and perform the occupant detection and counting function. We demonstrate experimentally that the combination of fused near-infrared phenomenology and fuzzy neural classification produces a robust solution to the problem of automatic vehicle occupant counting. We substantiate our argument by providing comparative experimental results for vehicle occupant counters based on visible, single near-infrared, and fused near-infrared bands. Interestingly, our proposed solution can find a more general applicability as the basis for a reliable face detector both indoors and outdoors.

Detecting Slope and Urban Potential Unstable Areas by Means of Multi-Platform Remote Sensing Techniques: The Volterra (Italy) Case Study

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“…If we have to handle different skin colors (white, black and yellow), the situation will become complex. In 1999~2000 Ioannis Pavlidis, Vassilios Morella and Nikolaos Papanikolopoulos [1] proposed a patent near-infrared imaging and fusion method which can simplify image processing algorithm without considering the skin-color. They also proposed a fuzzy neural network classifier to operate upon the fused near-infrared imagery and perform the occupant detection and counting function.…”

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