Shopper Analytics: A Customer Activity Recognition System Using a Distributed RGB-D Camera Network

Liciotti, Daniele; Contigiani, Marco; Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo; Placidi, Valerio

doi:10.1007/978-3-319-12811-5_11

Cited by 41 publications

(26 citation statements)

References 16 publications

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“…The two subsystems have complementary properties i.e., the radiofrequency localizer solves the occlusions that may occur in the computer vision detector, and the computer vision subsystem increases the accuracy of positions measured with the radiofrequency localizer. This model falls in the larger category of bimodal position and activity sensing systems also developed by other authors for analysis of shoppers [ 43 , 44 ], pedestrians [ 45 , 46 ], or just human pose recognition [ 47 ]. Both subsystems are independent and separately process the RF and RGBD sensors produced data.…”

Section: State-of-the-artmentioning

confidence: 99%

Assisted Living System with Adaptive Sensor’s Contribution

Smoleń

Augustyniak

2020

Sensors

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Multimodal sensing and data processing have become a common approach in modern assisted living systems. This is widely justified by the complementary properties of sensors based on different sensing paradigms. However, all previous proposals assume data fusion to be made based on fixed criteria. We proved that particular sensors show different performance depending on the subject’s activity and consequently present the concept of an adaptive sensor’s contribution. In the proposed prototype architecture, the sensor information is first unified and then modulated to prefer the most reliable sensors. We also take into consideration the dynamics of the subject’s behavior and propose two algorithms for the adaptation of sensors’ contribution, and discuss their advantages and limitations based on case studies.

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Section: State-of-the-artmentioning

confidence: 99%

Assisted Living System with Adaptive Sensor’s Contribution

Smoleń

Augustyniak

2020

Sensors

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“…The recent availability of affordable RGB-D cameras, together with depth information, has enabled significant improvement in scene modeling, estimation of human poses and obtaining good action recognition performance (Jiang and Saxena, 2013) (Liciotti et al, 2014) (Sturari et al, 2016). This topic is very challenging and important because understanding and tracking human behaviour through videos has several useful applications.…”

Section: Related Workmentioning

confidence: 99%

HMM-based Activity Recognition with a Ceiling RGB-D Camera

Liciotti

Frontoni

Zingaretti

et al. 2017

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods

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Abstract:Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.

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“…According to [19], it is possible to summarize the buying decision process into 5 steps: perception of the problem, research of information, evaluation of options, buying decision, post purchase behaviour. In this process, we cannot understate surroundings and spaces; customers are not always aware of interior layouts, way finding, and market displays.…”

Section: Pervasive Retail For Customer Behavior Analysismentioning

confidence: 99%

Low cost embedded system for increasing retail environment intelligence

Pierdicca

Liciotti

Contigiani

et al. 2015

2015 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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The success of pervasive smart environments lies in the capacity to involve visitors to interact with them. It is essential for retail stores. In this paper we describe the setting-up of a low cost system for the indoor localization and customer interaction, developed with a complex infrastructure of wireless embedded sensors. The creation of a responsive store allows customers to connect the real world to their smart devices and will overcome the lack of ubiquity in public spaces; furthermore, from in-venue analytics and proximity sensor it is possible to customize the user experience.First of all we describe the whole sensor network. We go in deep into the active beacon technology adopted for this study. Then, thanks to the analytics, we present a data evaluation with the aim of determining the best sensor arrangement, according to several user tests. Beside the strong enhancement of human interaction, the results of our essay demonstrate how embedded localization systems could be a useful source for data collection beside the strong enhancement of human interaction. This paper is focused to help retailers and insiders for many purposes such as products development or improvement, segmentation strategies and human behaviour analyses into such stores where the embedded computing augment the environment.

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Shopper Analytics: A Customer Activity Recognition System Using a Distributed RGB-D Camera Network

Cited by 41 publications

References 16 publications

Assisted Living System with Adaptive Sensor’s Contribution

Assisted Living System with Adaptive Sensor’s Contribution

HMM-based Activity Recognition with a Ceiling RGB-D Camera

Low cost embedded system for increasing retail environment intelligence

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