People Counting in Crowded Environment and Re-identification

Frontoni, Emanuele; Paolanti, Marina; Pietrini, Rocco

doi:10.1007/978-3-030-28603-3_18

Cited by 8 publications

(4 citation statements)

References 62 publications

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“…so, we use another algorithm, random forest, which simultaneously combines several decision trees quality features to make decisions. It is a forest of randomly generated decision trees [23]. Overfitting is a significant drawback of the decision tree method.…”

Section: Random Forest Regressormentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>

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Section: Random Forest Regressormentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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“…Lately, novel approaches have emerged for detecting people and understanding their behaviour automatically [19]. For example, some approaches (such as [20]) attempt to transform an unmodified WiFi radio infrastructure into a flexible sensing system for detecting the people moving indoors.…”

Section: Related Workmentioning

confidence: 99%

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

Berenguer

Ros²,

Gómez-Oliva³

et al. 2022

Electronics

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Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.

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“…The obtained deep learning results are compared with image processing algorithm: multi-level segmentation [75] and water filling [76]. Table 4 shows the results of algorithms in terms of precision, recall and F1-score.…”

Section: People Countingmentioning

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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People Counting in Crowded Environment and Re-identification

Cited by 8 publications

References 62 publications

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

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

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