Politag: Object Detection and Identification System using Polarized Light

• Main Authors: Cai-Jhih Luo, 羅才智
• Other Authors: 蔡欣穆
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/747pc2

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === This thesis presents PoliTag, a long-range object detection and identification system using polarized light and camera communications (CamCom), which modulate LED to transmit digital information to a commodity camera. Conventional methods for object detection and identifications usually depends on the spatial appearance of an object or a marker, and their performance quickly deteriorate as the distance to the object increases, since the small number of image pixels occupied by the spatial pattern no longer has sufficient resolution. In this thesis, we take a different approach; instead of relying on only a spatial pattern, we also take advantage of a temporal pattern, i.e., the modulated light wave. To extend the range supported by the system, we leverage polarized light, which can hide flickers from human eyes while allowing the camera to reliably receive the transmission even at long distance. We built the PoliTag prototype using off-the-shelf components: retroreflector, polarizer, and a liquid crystal cell (LCC) in the Tag, and an electronic polarizer and a commodity camera in the Reader. The LCC of the Tag modulates the polarization angle of the emitted light to generate a polarization pattern that allows detection and identification of the Tag. The electronic polarizer allows Reader to calculate polarization information for each pixel. The polarization information allows us to reduce the region-of-interest (ROI) in each captured image to very small areas that contain only highly polarized pixels, reducing the required computation and improving the detection and identification performance. As a result, our implementation is capable of real-time processing at 100 frames per second. Evaluation results of our prototype show that the system has a 86% identification rate at 30 meters in challenging outdoor scenarios, while the Tag can have 16 different identifications and occupies only a 6 by 6 pixels area.