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A typical LIDAR to scan for people. (Left) The position of people relative to the LIDAR, (Center) three types of scenarios, (Right) representation of their weight function.
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Context 1
... (a) most probably exists-indicating a contour which may correspond with the edge of a detected visitor, (b) must not exist-indicating certain points are empty, that is there are no observable entities, (c) undefined-indicating that a certain area is occupied by some observable entity. In such an area, the existence of any visitor is unclear. Fig. 4 ( Left and Center) illustrates the distinction between these three types of scenarios, obtained from the LIDARs provided information. Now, it is important to design a weighting scheme for each of the samples. The design of our weighting function is illustrated in Fig. 4 (Right) for the three mentioned scenarios. We assigned the weights ...
Context 2
... observable entity. In such an area, the existence of any visitor is unclear. Fig. 4 ( Left and Center) illustrates the distinction between these three types of scenarios, obtained from the LIDARs provided information. Now, it is important to design a weighting scheme for each of the samples. The design of our weighting function is illustrated in Fig. 4 (Right) for the three mentioned scenarios. We assigned the weights as C HIGH , C LOW and C MEDIUM to the data provided by the LIDARs for the scenarios-most probably exists, must not exist and undefined, respectively. With these assigned weights, we can easily identify the state of the observation area on whether any visitor exists or ...
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Citations
... Time is equally important for researchers to assess the visitor´s behavior. Comparing the visit´s timeframe and the span of time each visitor indulges in contemplating the works (Rashed et al., 2016). Time being analysed in clusters for the visit and the course would help us predict the typical behaviour of visitors in the coming exhibitions . ...
Given the considerable sums of money wasted on their creation, many museums are not well attended by the general public for a variety of reasons. The city of Bejaia museum, which is dedicated to the history and the Moudjahid (ancient Algerian soldiers of the liberation fight), has a lot of potential and it is now the only museum of city that provides this sort of service to visitors. However, it shows some dysfunctions that make the visited unpleasant, such as low tendance rates; causing its closure during the Covid19 period due to difficult flow management (Especially during event periods); we
propose a syntactic analysis with a state of the museum through this study, which will allow us to make a diagnosis and propose solutions to improve the exhibition for the covid-19 and the post covid 19
periods.
... Lanir et al. [1] presented an automatic tracking system based on RFID technology for studying the visitors' behavior at the Hecht Museum in Israel. Similarly, Rashed et al. used light detection and ranging (LIDAR) to track and analyze visitor behavior at the Ohara Museum of Modern Arts, Japan [8]. Although these technologies gave useful insights into how people behaved in a museum environment, they required equipment and device installation in both museum space and visitors. ...
The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors.
... They need additional data such as video recordings or manual records [25]. LiDAR cameras in museums have similar limitations for measuring social interactions with exhibitions, requiring significant processing tasks to get precise trajectories, deriving information mainly about highly concurred areas [26]. Finally, computer vision techniques to identify human traffic rely on RGB cameras to detect people's bodies and derive their trajectories, incurring privacy concerns and challenges such as occlusion and distortion. ...
Analysing the dynamics in social interactions in indoor spaces entails evaluating spatial–temporal variables from the event, such as location and time. Additionally, social interactions include invisible spaces that we unconsciously acknowledge due to social constraints, e.g., space between people having a conversation with each other. Nevertheless, current sensor arrays focus on detecting the physically occupied spaces from social interactions, i.e., areas inhabited by physically measurable objects. Our goal is to detect the socially occupied spaces, i.e., spaces not physically occupied by subjects and objects but inhabited by the interaction they sustain. We evaluate the social representation of the space structure between two or more active participants, so-called F-Formation for small gatherings. We propose calculating body orientation and location from skeleton joint data sets by integrating depth cameras. The body orientation is derived by integrating the shoulders and spine joint data with head/face rotation data and spatial–temporal information from trajectories. From the physically occupied measurements, we can detect socially occupied spaces. In our user study implementing the system, we compared the capabilities and skeleton tracking datasets from three depth camera sensors, the Kinect v2, Azure Kinect, and Zed 2i. We collected 32 walking patterns for individual and dyad configurations and evaluated the system’s accuracy regarding the intended and socially accepted orientations. Experimental results show accuracy above 90% for the Kinect v2, 96% for the Azure Kinect, and 89% for the Zed 2i for assessing socially relevant body orientation. Our algorithm contributes to the anonymous and automated assessment of socially occupied spaces. The depth sensor system is promising in detecting more complex social structures. These findings impact research areas that study group interactions within complex indoor settings.
... Tracking can be also used to understand how the flow of visitors inside a museum is oriented. In [13], the authors propose a method based on LIDAR to identify human beings and track their positions, body orientation, and movement trajectories in any public space. The system can accurately track the position of the visitor inside the museum. ...
Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.
... Very often, heat maps can also be found to represent captured people when automatic data collection is performed, e.g., in (Rajasekaran et al., 2020) to represent group activities in student dormitories. In (Rashed et al., 2016) heat maps are used to analyze which exhibit in a museum is particularly popular for visitors. Bolleter also uses heat maps to present his results of Wi-Fi tracking in public spaces to count people and map their stays and movements at the district level (Bolleter, 2017). ...
... This work has been extended in [18], where a routing mechanism is introduced in order to recommend a museum tour to the visitors for the purpose of optimizing their QoE. Rashed et al. [19] have implemented a light detection and ranging system to detect and track the visitors' positions and mobility patterns in the museum. Based on the latter collected information, the visitors' behavior during the museum touring is inferred and used in designing the museum exhibition to improve visitor QoE. ...
In this article, we address the problem of modeling and orchestrating the interactions between a museum and its visitors, viewing the system as a cyber–physical–social system (CPSS). In particular, the museum operator provides monetary rewards to the visitors in exchange for their contributions, which are expressed as their total number of provided feedback evaluations of visited exhibits over their touring time. The interactions among the museum operator and visitors are captured in appropriately designed utility functions following the principles of labor economics, while the visitors’ behavioral characteristics are utilized to define their unique types. Under such a setting and formulation, the goal of the museum operator is to optimize their profit and benefits while jointly satisfying the visitors’ quality of experience prerequisites as reflected via their utility functions. The corresponding optimization problem is treated and solved under the general and realistic cases of incomplete information, wherein the museum operator estimates the visitors’ types probabilistically. The resulting outcome, referred to as the “optimal contract”, jointly determines the visitors’ optimal contributions, as well as the museum operator’s optimal amount of personalized rewards provided to each visitor. The performance of the proposed approach is evaluated through modeling and simulation, and detailed numerical results are presented to demonstrate the key benefits of the proposed optimization approach versus either type-agnostic or heuristic alternatives.
... They need additional data such as video recordings or manual records [14], [16]. LiDAR cameras in museums have similar limitations for measuring social interactions with exhibitions, requiring significant processing tasks to get precise trajectories, deriving information mainly about highly concurred areas [17]. The second category is obstrusive as it is installed in peoples' bodies in the form of trackers and markers, interfering with their activities' natural behavior, especially when users are required to activate beacons to confirm their locations [18]. ...
Studying people's movement and social interaction in indoor spaces requires detecting where and when interactions happen. So far, sensor-based systems for detecting interactions focused on identifying physically occupied spaces, i.e., spaces determined by the presence of physically measurable objects. In contrast, our goal is to detect socially occupied spaces, i.e., spaces occupied not by people but by social interactions that they support. We focus on detecting F-Formations, a representation of the space structure between two or more potentially interacting building occupants. We propose a system that uses infrared depth cameras to anonymously derivate body orientation from skeleton joints. The orientation of the shoulders and spine, together with the face orientation and the temporal information on occupants' walking trajectories, is used to calculate the body orientation from which socially occupied space is identified. In a user study evaluating the system, we collected data in 32 patterns within two distinct cases: individuals and dyads and evaluated the system's accuracy. The evaluation included the intended orientation and the socially accepted orientation. Experimental results show accuracy well above 90% for assessing the socially-relevant body orientation. Our algorithm to detect body orientation contributes to the automated detection of socially occupied spaces. The system shows potential to detect more complex social structures. This would have an impact on research areas that study group interactions within complex indoor settings.
... All these tools are invasive and do not allow the visitor to have a direct approach to the works, thus altering the emotional impact of the observer. In other cases, the visitor is not asked to wear anything and a network of light detection and ranging (LIDAR) devices is used to detect visitors [14]. In this case, collaboration is not necessary for the visitor. ...
... Furthermore, the cameras required for implementing the system are off-the-shelf or phone cameras. From these observations, we understand that the cost of our entire system for tracking museum visitors can be extremely low, certainly lower, for example, than that of the system proposed in [14] where the cost of a single LIDAR scanner can be in the order of thousands of dollars. Moreover, based on the authors' experience, wearing a badge during a social event requires a minimal collaborative effort on the part of the visitor. ...
... Various information about visitors can be collected and associated with exhibits in a smart museum. The monitoring system tracks visitors' positions and movement patterns through museum art galleries with the goal of assisting museum personnel in analyzing visitors' activities, behaviors and experiences (Rashed et al., 2016). The collected behavior data are used for analysis and prediction of real museum visitors' interests and preferences (Lanir et al., 2017). ...
The chapter presents the authors' study on the smart museum concept. Semantic Web technology and ontology modeling methods are applied to construct advanced digital services, supporting the study and evolution of museum collections. The concept aims at significant increase of the information impact of museum exhibits by providing augmented annotations, identifying semantic relations, assisting the visitors to follow individual trajectories in exposition study, finding relevant information, opening the collection to knowledge from visitors. A museum collection is advanced to a knowledge base where new information is created and evolved by museum visitors and personnel. The chapter discusses reference information assistance services, which are oriented for use as mobile applications on users' smartphones. The proof-of-the-concept case study is the History Museum of Petrozavodsk State University. The pilot implementation demonstrates the feasibility of the smart museum concept in respect to the user mobility, service personalization, and collaborative work opportunity.
