Abstract
We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.
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Ong, KL., Lee, V.C.S. (2011). Real-Time Detection of Parked Vehicles from Multiple Image Streams. In: Fong, S. (eds) Networked Digital Technologies. NDT 2011. Communications in Computer and Information Science, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22185-9_24
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DOI: https://doi.org/10.1007/978-3-642-22185-9_24
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