Parking-stall vacancy indicator system, based on deep convolutional neural networks
By Sepehr Valipour, Mennatullah Siam, Eleni Stroulia, Martin Jägersand. 2016
86 citations recorded by the source corpus.
Parking-management systems, including services that recognize vacant stalls, can play a valuable role in reducing traffic and energy waste in large cities. Visual methods for detecting vacant parking spots are cost-effective options since they can take advantage of the cameras already available in many parking lots. However, visual-detection methods can be fragile and not easily generalizable. In this paper, we present a robust detection algorithm based on deep convolutional neural networks. We implemented and tested our algorithm on a large baseline dataset, and also tested on video feeds from web-accessible parking-lot cameras. Our detection method improved the state of the art AUC by 8.13%. It also showed robust performance in different testing scenarios including tests on public cameras. We have developed a fully functional system, from server-side image analysis to front-end user interface, to demonstrate the practicality of our method.
Subfields: Building and Construction, Computer Vision and Pattern Recognition
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Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, neutral): “Image Recognition [124] [58]–[60], [94], [95]”
- A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “) across centralized and distributed cloud computing frameworks, utilizing IoT devices and some novel mechanisms [67], [85], [95], [101], [134], [142], [160].”