Estimating the occupancy status of parking areas by counting cars and non-empty stalls

Mauro, Daniele Di; Furnari, Antonino; Patanè, Giuseppe; Battiato, Sebastiano; Farinella, Giovanni Maria

doi:10.1016/j.jvcir.2019.05.015

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…In contrast, object detection identifies objects of the same type in an image and pinpoints their locations [ 7 ]. The main purpose of object counting applications is to calculate the number of cars on a road or parking lot [ 8 , 9 , 10 , 11 ], the number of people in a crowded area [ 12 , 13 , 14 ], the number of goats and sheep in an livestock farm [ 15 , 16 ], and the number of apples in an cultivated area [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures

Özden

Bulut

Boğa

et al. 2023

Veterinary Sciences

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Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.

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Section: Introductionmentioning

confidence: 99%

Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures

Özden

Bulut

Boğa

et al. 2023

Veterinary Sciences

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“…Ref. [5] applies classification methods to images captured by fixed cameras in order to estimate the occupancy status. A prediction of parking lot occupancy is performed by [6] based on data from smart parking meters.…”

Section: Introductionmentioning

confidence: 99%

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

Hellekes

Kehlbacher

Díaz

et al. 2022

IET Intelligent Trans Sys

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Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.

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An accurate car counting in aerial images based on convolutional neural networks

Kilic

Öztürk

2021

J Ambient Intell Human Comput

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Estimating the occupancy status of parking areas by counting cars and non-empty stalls

Cited by 6 publications

References 13 publications

Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures

Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

An accurate car counting in aerial images based on convolutional neural networks

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