Computer Algebra Algorithms Applied to Computer Vision in a Parking Management System

López-Sastre, Roberto J.; Jiménez, Pedro Gil; Acevedo, F.J.; Bascón, Saturnino Maldonado

doi:10.1109/isie.2007.4374856

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…Considering this assumption does not hold for outdoor scenes, the limits of this approach are immediately clear. A more robust method was proposed in [34]. The authors used Gabor filters to train a classifier with images of unoccupied parking spaces under various lighting conditions.…”

Section: Related Workmentioning

confidence: 99%

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Nurullayev

Lee

2019

Sensors

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The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

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Section: Related Workmentioning

confidence: 99%

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Nurullayev

Lee

2019

Sensors

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“…Early studies used different types of features and classification algorithms to model empty spaces (Lin, Chen, and Liu 2006;Wu et al 2007;Huang and Wang 2010). Under different lighting conditions texture descriptors are more robust than color features (Sastre et al 2007;Almeida et al 2015). Ichihashi et al in particular dealt with various luminance conditions using images captured during different hours (Ichihashi et al 2009).…”

Section: Related Work Parking Space Detectionmentioning

confidence: 99%

Early Detection of Vacant Parking Spaces Using Dashcam Videos

Yeh

2019

AAAI

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A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.

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“…Other approaches are based on machine learning, in which feature extraction is followed by a classifier for assessing occupancy status. For instance, in [ 22 ], Gabor filters are used for extracting features; then, a training dataset containing images with different light conditions is used to achieve a more robust classification. More recently, approaches based on textural descriptors such as Local Binary Patterns (LPB) [ 23 ] and Local Phase Quantization (LPQ) [ 24 ] have appeared.…”

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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Computer Algebra Algorithms Applied to Computer Vision in a Parking Management System

Cited by 18 publications

References 17 publications

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Early Detection of Vacant Parking Spaces Using Dashcam Videos

An Intelligent Cooperative Visual Sensor Network for Urban Mobility

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