Real time car parking system using image processing

Banerjee, Sayanti; Choudekar, Pallavi; Muju, M. K.

doi:10.1109/icectech.2011.5941663

Cited by 54 publications

(14 citation statements)

References 2 publications

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“…Several methods have been proposed to build such a system [1][2][3][4][5][6][7], most of which are based on separation of foreground and background. Typically, vehicles are first extracted from background and then are tracked.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Illegal Parking Detection System Based on Deep Learning

Xie¹,

Wang²,

Chen³

et al. 2017

Proceedings of the 2017 International Conference on Deep Learning Technologies

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The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

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Section: Introductionmentioning

confidence: 99%

“…It copes well with lighting changes, but it is hard to design the SHOG feature, and cannot deal with complex weather conditions. [6] and [7] also falls into this category. However, extracting foreground by background segmentation is easily affected by environments, such as illumination changing and the weather.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Illegal Parking Detection System Based on Deep Learning

Xie¹,

Wang²,

Chen³

et al. 2017

Proceedings of the 2017 International Conference on Deep Learning Technologies

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“…Menemukan ruang kosong untuk memarkir mobil di area parkir dalam ruangan sering menjadi masalah, terdapat beberapa penelitian tentang teknik penentuan posisi mobil seperti yang tercantum dalam referensi [2,3,4,5,6,7,8,11].…”

Section: Pendahuluanunclassified

Pendeteksian Ruang Kosong Parkir di dalam Ruangan

2018

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Menemukan tempat parkir kosong di tempat parkir dalam ruangan seperti pusat perbelanjaan menjadi kesulitan banyak pengemudi, terutama saat jam sibuk di kota-kota besar. Dalam makalah ini, sebuah sistem deteksi kekosongan tempat parkir dalam ruangan diusulkan, dengan menggunakan sistem kamera yang melibatkan OpenCV untuk mempercepat waktu dalam mencari tempat parkir bagi pengemudi kendaraan dengan memberi mereka informasi lokasi dan tempat parkir. Sistem ini menggunakan metode deteksi objek statis, yaitu Haar-Like Cascade Classifier yang dikombinasikan dengan Hough Line Detection untuk mengidentifikasi area parkir kosong dari gambar parkir yang diambil secara real time melalui kamera IP atau kamera USB. Sistem ini dirancang untuk disematkan dengan sistem manajemen parkir sebuah bangunan sebagai alat yang menyediakan tempat parkir untuk membantu pengemudi kendaraan memasuki area parkir. Index Terms—Haar-Like Cascade Classifier, Hough Line Detection, Sistem Maanajemen Parkir

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“…The first presented solutions were based on an edge-computing approach in which whole images or high-level extracted features were processed by high computational nodes located at the edge of the monitoring system. Works such as [ 10 , 11 ] are just an example of systems following this approach. More recent approaches leverage powerful visual sensor nodes, thus proposing solutions in which images are fully processed on-board, thus exploiting fog-computing capabilities.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Cooperative Visual Sensor Network for Urban Mobility

Leone

Moroni

Pieri

et al. 2017

Sensors

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Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

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Real time car parking system using image processing

Cited by 54 publications

References 2 publications

Real-Time Illegal Parking Detection System Based on Deep Learning

Real-Time Illegal Parking Detection System Based on Deep Learning

Pendeteksian Ruang Kosong Parkir di dalam Ruangan

An Intelligent Cooperative Visual Sensor Network for Urban Mobility

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