Vision-based entrance detection in outdoor scenes

Talebi, Mehdi; Vafaei, Abbas; Monadjemi, Amirhassan

doi:10.1007/s11042-018-5846-3

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…Recent work shows the feasibility of utilizing street-level imagery in assessing structural changes in urban areas [1], inferring subjective properties of urban areas such as safety, liveliness, and attractiveness [8], mapping urban greenery [18][19][20], geo-locating high-density urban objects [33], or estimating citylevel travel patterns [11]. Other works applied computer vision techniques to Google Street View images for inferring the socioeconomic attributes of neighbourhood in US [9], finding morphological characteristics to distinguish European cities [7], detection of building entrance in outdoor scenes [40], or detection and classification of traffic signs [3]. Yu et.…”

Section: Related Workmentioning

confidence: 99%

Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

Noorian

Qiu

Psyllidis

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output. Our method showed a recall of 89.05% and a precision of 88.22% on a real-world dataset of street-level images, which experimentally demonstrated that our approach achieves human-level accuracy while having a remarkable run-time efficiency compared to methods such as Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot Detector (SSD).

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Section: Related Workmentioning

confidence: 99%

Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

Noorian

Qiu

Psyllidis

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…The reminding region is considered as the candidate of entrance, which is then evaluated by their proposed probabilistic model. Recently, Talebi, Vafaei, and Monadjemi (2018) presented a vision-based method for detecting building entrances with outdoor images. They first converted the RGB image into gray-scale image, from which the vertical and horizontal line segments can be detected by using Line Segment Detector (LSD) algorithm.…”

Section: Related Workmentioning

confidence: 99%

Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Noskov

Fan

et al. 2021

International Journal of Geographical Information Science

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The entrance of buildings is an important feature that connects their internal and external environments. Most frequently, automatic approaches for detecting building entrances are based on street-level images, which, however, are not widely available. To address this issue, we propose a more general approach for inferring the location of the main entrance of public buildings based on the association between spatial elements extracted from OpenStreetMap. In particular, we adopt three binary classification approaches: Weighted Random Forest, Balanced Random Forest, and SmoteBoost to model the association relationship. The features considered in the classification are of two types: (1) intrinsic features derived from the footprint, such as the distance to the centroid of the footprint, and (2) extrinsic features derived from spatial contexts, such as the shortest path distance to the main roads. Extensive experiments have been conducted on 320 public buildings with an average perimeter of 350 meters. The experimental results showed that a mean linear distance error of 21 meters and a mean path distance error of 22 meters were achieved by using the Weighted Random Forest and Balanced Random Forest models, ruling out 90% of the incorrect locations of the main entrance at buildings. Our work finds relevance, for example, in saving pedestrians' way-finding efforts.

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“…For Edge detection, we found two algorithms methods; canny edge detector that is used to detect doors (Sivan & Darsan, 2016) and line segment detector, which is also used to detect doors with an accuracy rate of up to 93.2% for the ImageNet dataset (Talebi & Vafaei, 2018). For objects and obstacle detection, we found three algorithms; first is CNN to recognize the color and sign of traffic got mAP of 96% % (Li, Cui, Rizzo, Wong, & Fang, 2020); in another research, CNN is also used to detect objects, but not accurate for multi objects in one scene, so they implemented RCNN (Balasuriya, Lokuhettiarachchi, Ranasinghe, Shiwantha, & Jayawardena, 2017), second is YOLOv1 used to detect objects and obstacles and the detection rate is up to 89% for all kind of obstacles (Mocanu, Tapu, & Zaharia, 2017), and third is YOLOv3 also used to detect objects, and the mAP is 73.19% (Afif, Ayachi, Pissaloux, Said, & Atri, 2020), and in the other research the accuracy rate is up to 95.19% (Joshi, Yadav, Dutta, & Travieso-Gonzalez, 2020) and 92% (Mahmud, Sourave, Islam, Lin, & Kim, 2020).…”

Section: Proposed Algorithm In CV Usedmentioning

confidence: 99%

“…There are also two other studies that help VIP to detect doors (Sivan & Darsan, 2016) (Talebi & Vafaei, 2018), and another research focus on helping VI to detect the colors of pedestrian signals (Li, Cui, Rizzo, Wong, & Fang, 2020). The complete summary can be seen in appendix Table 8.…”

Section: Supported Tasksmentioning

confidence: 99%

Computer Vision for Supporting Visually Impaired People: A Systematic Review

Anthony

Kusnadi

2021

EMACS Journal

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Globally around the world in 2010, the number of people of all ages visually impaired is estimated to be 285 million, of whom 39 million are blind according to the study of World Health Organization (Global Data on Visual Impairments, 2010). Visual impairment has a significant impact on individuals’ quality of life, including their ability to work and to develop personal relationships. Almost half (48 %) of the visually impaired feel “moderately” or “completely” cut off from people and things around them (Hakobyan, Lumsden, O’Sullivan, & Bartlett, 2013). We believe that technology has the potential to enhance individuals’ ability to participate fully in societal activities and to live independently. So, in this paper we focused to presents a comprehensive literature review about different algorithms of computer vision for supporting blind/vision impaired people, different devices used and the supported tasks. From the 13 eligible papers, we found positive effects of the use of computer vision for supporting visually impaired people. These effects included: the detection of obstacles, objects, door and text, traffic lights, sign detections and navigation. But the biggest challenge for developers now is to increase the speed of time and improve its accuracy, and we expect the future will have a complete package or solution where blind or vision impaired people will get all the solution together (i.e., map, indoor-outdoor navigation, object recognition, obstacle recognition, person recognition, human crowd behavior, crowd human counting, study/reading, entertainment etc.) in one software and in hand-held devices like android or any handy devices.

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Vision-based entrance detection in outdoor scenes

Cited by 9 publications

References 26 publications

Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Computer Vision for Supporting Visually Impaired People: A Systematic Review

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