Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment

Liciotti, Daniele; Paolanti, Marina; Pietrini, Rocco; Frontoni, Emanuele; Zingaretti, Primo

doi:10.1109/icpr.2018.8545397

Cited by 36 publications

(24 citation statements)

References 19 publications

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“…retail, airport, station, etc.) (Liciotti et al, 2018). Another work (Paolanti et al, 2018) that extract anthropometric features for the recognition of people using a top-view camera, reducing the problem of occlusions, propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images.…”

Section: Related Workmentioning

confidence: 99%

How to Extract Useful Information About the Decay of Bass Relieves in Archaeological Area

Malinverni

Pierdicca

Stefano

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Cultural Heritage goods represent the memory and the history of the civilization. Notwithstanding, there are not sufficient public resources to guarantee their preservation and maintenance. Nowadays between several geomatic techniques available, the pillar for the preservation of mankinds heritage is the low cost close photogrammetric acquisition. The advantages of virtual reconstructions based on Multi View Stereo (MVS) and Structure from Motion (SfM) algorithms is extended from the heritage documentation to its virtualization or modelling. The digital preservation of archaeological sites is committed in more agile and friendly procedures that give automatic extraction of information to perform in depth analysis over ancient artefacts. In the field of CH research, the characterization and classification of the conservation state of the materials composing the surface of the artefacts are essential to study their damage. The first step for conservation state of a goods is the study of the changes in different times. The possibility to automatically study this time modification due to different factor represents a key point for the archaeologists' work. With this in mind, the aim of this work is to propose a completely automatic methods for change detection between three data set acquired in different époques. The work flow applied is based on the unsupervised clustering techniques applied on a combination of two type of differences images. The results, unlike the objective, demonstrate that the unsupervised methods are not effectiveness in the CH study, instead of the supervised methods that outperforms in terms of reliability of results.

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Section: Related Workmentioning

confidence: 99%

How to Extract Useful Information About the Decay of Bass Relieves in Archaeological Area

Malinverni

Pierdicca

Stefano

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Over the past years, machine-learning and feature-based tools were developed with the aim of learning shopper skills in intelligent retail environments. Each application uses RGB-D cameras in a top-view configuration that are installed in different locations of a given store, providing large volumes of multidimensional data that can be used to determine statistics and deduce insights [4][5][6]. These data are analysed with the aim of examining the attraction (the level of attraction that the shopper is showing for a store category based on the rate between the total amount of shoppers that entered the store and those who passed by the category), the attention (the amount of time that shoppers spend in front of a brand display) and the action (the consumers who go into the store and interact with the products, those who buy a product and those who interact with a product without buying it).…”

Section: Introductionmentioning

confidence: 99%

“…In this work, a novel VRAI 1 deep learning framework is introduced with the goal of improving our existing applications [4][5][6], and it is suggested that this evolution of machine intelligence provides a solid guide for discovering powerful insights in the current big data era.…”

Section: Introductionmentioning

confidence: 99%

Deep understanding of shopper behaviours and interactions using RGB-D vision

Paolanti

Pietrini

Mancini

et al. 2020

Machine Vision and Applications

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In retail environments, understanding how shoppers move about in a store’s spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers’ movements, mean that accurately measuring shopper behaviour is still challenging. Over the past years, machine-learning and feature-based tools for people counting as well as interactions analytic and re-identification were developed with the aim of learning shopper skills based on occlusion-free RGB-D cameras in a top-view configuration. However, after moving into the era of multimedia big data, machine-learning approaches evolved into deep learning approaches, which are a more powerful and efficient way of dealing with the complexities of human behaviour. In this paper, a novel VRAI deep learning application that uses three convolutional neural networks to count the number of people passing or stopping in the camera area, perform top-view re-identification and measure shopper–shelf interactions from a single RGB-D video flow with near real-time performances has been introduced. The framework is evaluated on the following three new datasets that are publicly available: TVHeads for people counting, HaDa for shopper–shelf interactions and TVPR2 for people re-identification. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods (accuracy of 99.5% on people counting, 92.6% on interaction classification and 74.5% on re-id), bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.

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“…The activity of segmentation is responsible for grouping the point clouds into subsets, named segments, on the base of one or more common features, that are geometric, radiometric, and so on. The subsequent classification procedure classifies the segments to a class taking into account proper criteria (Grilli et al, 2017), (Liciotti et al, 2018). In other words, from the point of view of automatic recognition techniques, the segmentation process has the task to group point clouds in homogeneous regions with the same properties, while the classification procedure labels the different groups.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Semantic Segmentation of 3d Point Cloud

Malinverni

Pierdicca

Paolanti

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.</p>

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Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment

Cited by 36 publications

References 19 publications

How to Extract Useful Information About the Decay of Bass Relieves in Archaeological Area

How to Extract Useful Information About the Decay of Bass Relieves in Archaeological Area

Deep understanding of shopper behaviours and interactions using RGB-D vision

Deep Learning for Semantic Segmentation of 3d Point Cloud

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