Localization and Tracking Using Camera-Based Wireless Sensor Networks

Abstract

This chapter presents various methods for object detection, localization and tracking that use a Wireless Sensor Network (WSN) comprising nodes endowed with low-cost cameras as main sensors. More concretely, it focuses on the integration of WSN nodes with low-cost micro cameras and describes localization and tracking methods based on Maximum Likelihood and Extended Information Filter. Finally, an entropy-based active perception technique that balances perception performance and energy consumption is proposed. Target localization and tracking attracts significant research and development efforts. Satellite-based positioning has proven to be useful and accurate in outdoor settings. However, in indoor scenarios and in GPS-denied environments localization is still an open challenge. A number of technologies have been applied including inertial navigation (Grewal et al., 2007), ultra-wideband (Gezici et al., 2005) or infrared light signals (Depenthal et al., 2009), among others. In the last decade, the explosion of ubiquitous systems has motivated intense research in localization and tracking methods using Wireless Sensor Networks (WSN). A good number of methods have been developed based on Radio Signal Strength Intensity (RSSI) (Zanca et al., 2008) and ultrasound time of flight (TOF) (Amundson et al., 2009). Localization based on Radio Frequency Identification (RFID) systems have been used in fields such as logistics and transportation (Nath et al., 2006) but the constraints in terms of range between transmitter and reader limits its potential applications. Note that all the aforementioned approaches require active collaboration from the object to be localized -typically by carrying a receiverwhich imposes important limitations in some cases. Also, recently, multi-camera systems have attracted increasing interest. Camera based localization has high potentialities in a wide range of applications including security and safety in urban settings, search and rescue, and intelligent highways, among many others. In fact, the fusion of the measurements gathered from distributed cameras can reduce the uncertainty of the perception, allowing reliable detection, localization and tracking systems. Many efforts have been devoted to the development of cooperative perception strategies exploiting the complementarities among distributed static cameras at ground locations (Black & Ellis, 2006), among cameras mounted on mobile robotic platforms (Shaferman & Shima, 2008) and among static cameras and cameras onboard mobile robots (Grocholski et al., 2006).