Sensor networks and artificial intelligence for real time motion analysis

Abstract

This paper addresses the problem of motion analysis performed from digital data captured by a network of motion sensors distributed over a three-dimensional field of interest. Motion analysis means performing motion detection, motion-oriented classification, estimation, and prediction of kinematic parameters, tracking to build trajectories, and warning of the occurrence of potential abnormalities, incidents or accidents. Kinematic parameters are defined as spatial and temporal positions, velocity, scale and orientation. The entire system can be decomposed into three major components. First, a network of sensors captures and generates all relevant motion information. Second, a tree-structured telecommunication system concentrates all motion information to a data sink or gateway. Third, an Artificial Intelligence (A.I.) in a remote monitoring center processes the entire data stream transmitted from the gateway. The A.I. is composed of three major components: a Simulating Software, a Deep Learning System, and an Expert System. This paper addresses the structural relation between motion sensor network and artificial intelligence in order to display on a screen a complete and real time motion analysis of the events taking place in a three dimensional field of interest. This work will address and compare different motion sensors. The reference network being a network made of motion sensors based on passive photodetection. Other sensor networks of interest are networks based on active detection namely ultrasonic waves (SONAR), microwaves (RADAR) and lasers (LIDAR). A limited amount of video cameras turns out to the unavoidable with any motion sensor net- works either active or passive, distributed or localized. Video cameras are required to produce high resolution images allowing pattern recognition and motion disambiguation. To conclude, a comparison is presented of different distributed systems that perform motion analysis through different potential technologies for motion sensor networks. To efficiently network in real time with an A. I., two main challenging questions are raised that are related first to the motion information structure, and second, to the amount to be transmitted. Distributed passive photodetection sensor networks are optimal solutions for long term indoor or short term outdoor analyses. Active sensor networks are optimal solution to extend long term motion analysis to surrounding outdoors.