Count on Me: Learning to Count on a Single Image

Setti, Francesco; Conigliaro, Davide; Tobanelli, Michele; Cristani, Marco

doi:10.1109/tcsvt.2017.2656718

Cited by 11 publications

(15 citation statements)

References 39 publications

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“…Specific pedestrian detection techniques have been designed to work in crowded scenes [29], [55], [97], [106], where saliency-based masks are often preferred to skeleton-based representations. When the image resolution becomes too low to spot single people, regression-based approaches are employed [12], [15], [54], [80], [92], [104], [111], providing in some case density measures [73], [86], [87], [110]. This information, merged with a geometric model of the scene, will directly lead to a measure of the average SD in the field of view.…”

Section: B Person Detection and Pose Estimationmentioning

confidence: 99%

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The Visual Social Distancing Problem

et al. 2020

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One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, governments are adopting restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a potential threat. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We first point out that measuring VSD is not only a geometrical problem, but it also implies a deeper understanding of the social behaviour in the scene. The aim is to truly detect potentially dangerous situations while avoiding false alarms (e.g., a family with children or relatives, an elder with their caregivers), all of this by complying with current privacy policies. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate a path to research new Computer Vision methods that can possibly provide a solution to such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.

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Section: B Person Detection and Pose Estimationmentioning

confidence: 99%

“…As for the modelling of crowd, some approaches allow to estimate the number of individuals [12], [15], [54], [80], [104] or their density [86], [87], [110]. Social Signal Processing approaches for large gatherings focus specifically on common-focused formations (i.e.…”

Section: Visual Social Distance Characterizationmentioning

confidence: 99%

The Visual Social Distancing Problem

et al. 2020

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“…Specific pedestrian detection techniques have been designed to work in crowded scenes [53,57,32,20], where skeleton-based representations are often dropped in favor of saliency-based masks, especially focusing on heads. When the image resolution becomes too low to spot single people, regression-based approaches are employed [11,31,46,51], providing in some cases density measures [48,43,47]. This information, merged with a geometric model of the scene, can lead to a solution for measuring the average SD in the field of view.…”

Section: Related Workmentioning

confidence: 99%

Single Image Human Proxemics Estimation for Visual Social Distancing

Aghaei

Bustreo

Wang

et al. 2020

Preprint

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In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-automatic solution to approximate the homography matrix between the scene ground and image plane. With the estimated homography, we then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances using the length of their body-parts. Inter-personal distances are further locally inspected to detect possible violations of the social distancing rules. We validate our proposed method quantitatively and qualitatively against baselines on public domain datasets for which we provided groundtruth on interpersonal distances. Besides, we demonstrate the application of our method deployed in a real testing scenario where statistics on the inter-personal distances are currently used to improve the safety in a critical environment.

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“…The object counting process in image processing can be performed by object detection, regression, and segmentation techniques. All of these approaches require a machine learning process on labelled data to build a detection model, thereby predicting the number of target objects in an image [ 13 ]. Global regression-based VOC (GR-VOC) and density estimation based VOC (DE-VOC) are two significant techniques using supervised approaches [ 17 ].…”

Section: Introduction and Backgroundsmentioning

confidence: 99%

“…Convolutional neural networks (CNN) and hardware-accelerated optimisation can improve the performance of regression-based counting approach significantly [ 11 ]. An innovative framework to count objects without any preliminary training step was proposed and can count multiple object types [ 13 ]. To minimize the data preparation costs introduced by labelling training data of regression-based approaches, an unsupervised approach was introduced to count objects without object recognition [ 7 ].…”

Section: Introduction and Backgroundsmentioning

confidence: 99%

New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devices

Nguyen

Sun

2021

Communications in Computer and Information Science

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New Zealand maintains excessive effort to organise the sustainable development of its marine resources, wildlife, and ecological environment. New Zealand has stringent rules to control fishing and to protect the continued growth of marine inhabitants. Fishing inspections, such as identifying and counting shellfish, are part of the daily routine of many New Zealand Fisheries officers. It is however considered labour-intensive and time-consuming work. This project, thus, develops a touch-less shellfish detection and counting web/mobile application on handheld devices using Mask R-CNN to assist New Zealand Fisheries officers in recognising and totalling shellfish automatically and accurately. New Zealand shellfish species are different from other places in the World. Thus, this study firstly investigates the best deep learning model to use for New Zealand shellfish recognition and detection. Selected shellfish dataset is collected from a local fish market in Auckland and trained by using the chosen artificial neural network. At last, a portable system is built to support Fisheries officers to count shellfish quickly and accurately. At this current stage, a web-based application has been successfully deployed at a local server (cvreact.aut.ac.nz) in which users can upload target objects to get results related to three major shellfish species including cockle, tuatua, and mussel. In the near future, this proposed model is scaled up to recognise more species to cover the popular shellfish species in New Zealand, thus benefiting the aquaculture as well.

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Count on Me: Learning to Count on a Single Image

Cited by 11 publications

References 39 publications

The Visual Social Distancing Problem

The Visual Social Distancing Problem

Single Image Human Proxemics Estimation for Visual Social Distancing

New Zealand Shellfish Detection, Recognition and Counting: A Deep Learning Approach on Mobile Devices

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