Visual identification of objects using cameras requires precise detection, localization, and recognition of the objects in the field-of-view. The visual identification problem is very challenging when the objects look identical and features between distinct objects are indistinguishable, even with state-of-the-art computer vision techniques. The problem becomes significantly more challenging when the objects themselves do not carry rich geometric and photometric features, for example, in visual identification and tracking of light emitting diodes (LED) for visible light communication (VLC) applications. In this paper, we present a camera based visual identification solution where objects or regions of interest are tagged with an actively transmitting LED. Motivated by the concept of pilot symbols, typically used for synchronization and channel estimation in radio communication systems, the LED actively transmits unique pilot symbols which are detected by the camera across a series of image frames using our proposed spatio-temporal correlation based algorithm. We setup the visual identification as a problem of localization of the LED on the camera image, which involves identifying the (<i>pixels</i>) and the <i>unique ID</i> corresponding to the LED. In this paper, we present the algorithm and trace-based evaluation of the identification accuracy under real-world conditions including indoor, outdoor, static and mobile scenarios. In addition to micro-benchmarking the localization accuracy of our technique across different parameter configurations, we show that our technique outperforms comparative techniques, including, color based detection, support-vector machine based (SVM) machine learning, and you only look once (YOLO), which is a state-of-the-art convolutional neural network (CNN) deep learning based object identification tool.
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Oct 1, 2022
Tingwei Lu + 6
Open Access