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Social robots in our daily surroundings, like personal guides, waiter robots, home helpers, assistive robots, and telepresence/teleoperation robots, are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior, and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other, and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. There are many theories which study group formations and proxemics; one such theory is f-formation which could be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining some of the possibly more important concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in the current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain. The official website for this work is available at: https://github.com/HrishavBakulBarua/Social-Robots-F-formation [ABSTRACT FROM AUTHOR]

Group detection is a critical yet challenging task in video-based applications such as surveillance analysis, especially in crowded and dynamic environments where complex pedestrian interactions occur. Traditional trajectory-based methods often struggle with occlusions and overlapping behaviors, leading to inaccurate group identification. To address these limitations, we propose a novel algorithm that integrates an optimized YOLOv8 model with DeepSORT tracking, enhancing both detection accuracy and real time performance. Our approach uniquely combines high-precision object detection with stable multi-object tracking, ensuring consistent identification of individuals and groups over time, even in high-density scenarios. Additionally, we introduce an innovative method of constructing an adjacency matrix by integrating Euclidean distances and bounding box diagonal ratios, which is transformed into a graph to intricately analyze and predict complex group dynamics in real time. Experimental results on real-world airport CCTV footage demonstrate that our method significantly outperforms existing approaches, achieving higher precision and recall rates. Furthermore, the algorithm operates efficiently on standard hardware, indicating strong practical feasibility for real-time applications in public spaces. While challenges such as misclassification due to incomplete data annotations and occlusions remain, our study showcases the potential of integrating spatial and temporal data to advance real-time group detection and tracking, aiming to improve crowd management systems in public spaces.

In this article, we are interested in detecting latent group structures and significant covariates in a high-dimensional panel data model with both individual and time fixed effects. The slope coefficients of the model are assumed to be subject dependent, and there exist group structures where the slope coefficients are homogeneous within groups and heterogeneous between groups. We develop a penalized estimator for recovering the group structures and the sparsity patterns simultaneously. We propose a new algorithm to optimize the objective function. Furthermore, we propose a strategy to reduce the computational complexity by pruning the penalty terms in the objective function, which also improves the accuracy of group structure detection. The proposed estimator can recover the latent group structures and the sparsity patterns consistently in large samples. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated with a real dataset. [ABSTRACT FROM AUTHOR]

Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users' group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users' static preference features while ignoring the variation of users' interest over time when finding the group structure in the network; second, group division based on users' topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users' dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users' dynamic preferences, and yield better results of group detection than that of existing algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel framework for identifying a group of intrusions in the context of the Internet of Things (IoT). It combines both deep learning and decomposition. A set of data is first collected and a recurrent neural network is used to estimate the different individual intrusions. These individual intrusions are then used to identify a group of outliers based on a decomposition strategy. As case studies, the proposed solutions have been experimentally evaluated using two computer network intrusion datasets, namely (1) IDS 2018, and (2) LUFlow. The results show the benefits of the proposed framework and clear superiority in comparison to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human–Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively. [ABSTRACT FROM AUTHOR]

Group detection is gaining popularity as it enables variousXzX applications ranging from marketing to urban planning. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be identified as groups. Moreover, RSSI is vulnerable to many factors like the human body attenuation and thus is unreliable in crowded scenarios. In this work, we propose a behavior-aware group detection system (BaG). BaG fuses people’s mobility information and smartphone usage behaviors. We observe that people in a group tend to have similar phone usage patterns. Those patterns could be effectively captured by the proposed feature: number of bursts (NoB). Unlike RSSI, NoB is more resilient to environmental changes as it only cares about receiving packets or not. Besides, both mobility and usage patterns correspond to the same underlying grouping information. We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously. Experimental results indicate BaG outperforms baseline approaches by $3.97\% \sim 15.79\%$ 3. 97 % ∼ 15. 79 % in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness. [ABSTRACT FROM AUTHOR]

Motion pattern segmentation for crowded video scenes is an open problem because of the inability of existing approaches to tackle unpredictable crowd behaviour across varied scenes. To address this problem, we propose a Spatio-Angular Density-based Clustering (SADC) approach, which performs motion pattern segmentation by clustering the spatial and angular information obtained from the input trajectories. The k-nearest neighbours of each trajectory and the angular deviation between trajectories constitute the spatial and angular information, respectively. Effective integration of the spatio-angular information with an improvised density-based clustering algorithm makes this approach scene-independent. The performance of most clustering algorithms in the literature is parameter-driven. Choosing a single parameter value for different types of scenes decreases the overall clustering performance. In this article, we have shown that our approach is robust to scene changes using a single threshold, and, through the analysis of parameters across eight commonly occurring crowded scenarios, we point out the range of thresholds that are suitable for each scene category. We evaluate the proposed approach on the benchmarked CUHK dataset. The experimental results show the superior clustering performance and execution speed of the proposed approach when compared to the state-of-the-art over different scene categories.

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.

With the advent of age of big data,the application fields of recommendation systems have become increasingly widespread.The recommendation service object of the group recommendation systems are expanded from a single user to a group of members,and becoming one of the research hotspots in the recommendation system fields.The group recommendation system needs to consider the preferences of all group members,and to fuse the preferences of the members,and to alleviate the conflicts of preferences among them,so that the recommendation results satisfy all group members as much as possible.The recent research progress of the group recommendation was mainly reviewed.The frontiers of group classification,group detection and group prediction recommendation were summarized.And main factors of the group recommendations were generalized.Finally,the research points of group recommendation and its prospects were separately elaborated.

People in crowd scenes always exhibit consistent behaviors and form collective motions. The analysis of collective motion has motivated a surge of interest in computer vision. Nevertheless, the effort is hampered by the complex nature of collective motions. Considering the fact that collective motions are formed by individuals, this paper proposes a new framework for both quantifying and detecting collective motion by investigating the spatio-temporal behavior of individuals. The main contributions of this work are threefold: 1) an intention-aware model is built to fully capture the intrinsic dynamics of individuals; 2) a structure-based collectiveness measurement is developed to accurately quantify the collective properties of crowds; 3) a multi-stage clustering strategy is formulated to detect both the local and global behavior consistency in crowd scenes. Experiments on real world data sets show that our method is able to handle crowds with various structures and time-varying dynamics. Especially, the proposed method shows nearly 10% improvement over the competitors in terms of NMI, Purity and RI. Its applicability is illustrated in the context of anomaly detection and semantic scene segmentation. [ABSTRACT FROM AUTHOR]

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people's trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP. [ABSTRACT FROM AUTHOR]

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic, but most of them have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this study, a Multiview-based Parameter Free framework (MPF) is proposed. Based on the L1-norm and L2-norm, we design two versions of the multiview clustering method, which is the main part of the proposed framework. This paper presents the contributions on three aspects: (1) a new structural context descriptor is designed to characterize the structural properties of individuals in crowd scenes; (2) a self-weighted multiview clustering method is proposed to cluster feature points by incorporating their orientation and context similarities; and (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. The effectiveness of the proposed framework is evaluated on real-world crowd videos, and the experimental results show its promising performance on group detection. In addition, the proposed multiview clustering method is also evaluated on a synthetic dataset and several standard benchmarks, and its superiority over the state-of-the-art competitors is demonstrated. [ABSTRACT FROM AUTHOR]

In an organization, individuals prefer to form various formal and informal groups for mutual interactions. Therefore, ubiquitous identification of such groups and understanding their dynamics are important to monitor activities, behaviors, and well-being of the individuals. In this paper, we develop a lightweight, yet near-accurate, methodology, called GroupSense, to identify various interacting groups based on collective sensing through users’ smartphones. Group detection from sensor signals is not straightforward because users in proximity may not always be under the same group. Therefore, we use acoustic context extracted from audio signals to infer the interaction pattern among the subjects in proximity. We have developed an unsupervised and lightweight mechanism for user group detection by taking cues from network science and measuring the cohesivity of the detected groups regarding modularity. Taking modularity into consideration, GroupSense can efficiently eliminate incorrect groups, as well as adapt the mechanism depending on the role played by the proximity and the acoustic context in a specific scenario. The proposed method has been implemented and tested under many real-life scenarios in an academic institute environment, and we observe that GroupSense can identify user groups with on an average $0.9(\pm 0.14)$ 0. 9 (± 0. 14) $F_1$ F 1 -Score even in a noisy environment. [ABSTRACT FROM AUTHOR]

In this paper, a very low complexity Lattice Reduction technique, called Dual Shortest Longest Vector algorithm (SLV), is adopted to improve the Bit Error Rate (BER) performance of the Minimum Mean Square Error (MMSE) detector in high-load Massive MIMO systems, whereby resulting in the so-called SLV-aided MMSE (MMSE–SLV) detector. An efficient combination scheme of Generalized Group Detection (GGD) algorithm and the MMSE–SLV, called MMSE–GGD–SLV, is further proposed to enhance BER performance of the system more significantly. In order to do so, we first convert the Group Detection approach to the generalized one (GGD) by creating an arbitrary number of sub-systems. Then, an additional operation, i.e., channel matrix sorting, is applied to the GGD to reduce the error propagation between sub-systems. To make the detection complexities of the MMSE–GGD–SLV detector more practical, the MMSE–SLV detection procedure is only applied to the first sub-system. Various BER performance simulations and complexity analysis show that both the MMSE–GGD–SLV and the MMSE–SLV detectors noticeably outperform their conventional MMSE counterpart, yet at the cost of higher detection complexities. However, their complexities are kept at acceptable levels, which are much lower than those of the conventional BLAST detector. Therefore, the proposed detectors are very good candidates for signal recovery in high load Massive MIMO systems. [ABSTRACT FROM AUTHOR]

Understanding occupant activities in a building is essential for building management systems to provide occupants with comfort and intelligent indoor environment. However, current occupant activity recognition mainly focuses on individual activity. Group activity recognition indoors has gained little attention, but remains of paramount importance, such as working together, taking classes, and discussions. In this paper, we propose a framework for group activity detection and recognition (i.e., GADAR framework) using smartphone sensors and Bluetooth beacons data. This framework consists of the following four layers: user layer, data package layer, processing layer, and output layer. As individuals within the group show similarity in motion, audio, and proximity, such similarity values are calculated and clustered into groups using hierarchical clustering. The framework then extracts the role, motion, speaking and location features from the clustered groups to distinguish different group activities. Decision tree classifier was selected to recognize the group activity that the group is engaged in. An experiment was conducted to identify the following three common group activities: taking class, seminar, and discussion. The result shows that the proposed GADAR framework could provide more than 89% accuracy in group detection and 89% accuracy in recognizing group activity. • We identify four challenges in group activity recognition considering group properties. • A group activity detection and recognition framework with four layers was proposed. • A prototype system was developed based on the framework and tested in a real setting. • The system recognized class, discussion, and seminar activities with 89% accuracy. • Four future research directions were discussed based on the case study. [ABSTRACT FROM AUTHOR]

The detection of groups of individuals is attracting the attention of many researchers in diverse fields, from automated surveillance to human-computer interaction, with a growing number of approaches published every year. Unexpectedly, the evaluation metrics for this problem are not consolidated, with some measures inherited from the people detection field, other from clustering, other designed specifically for a particular approach, thus lacking in generalization and making the comparisons between different approaches hard to be carried out. Moreover, most of the existent metrics are scarcely expressive, addressing groups as they are atomic entities, ignoring that they may have different cardinalities, and that group detection approaches may fail in capturing the exact number of individuals that compose it. This paper fills this gap presenting the GROup DEtection (GRODE) metrics, which formally define precision and recall on the groups, including the group cardinality as a variable. This gives the possibility to investigate aspects never considered so far, such as the tendency of a method of over- or under-segmenting, or of better dealing with specific group cardinalities. The GRODE metrics have been evaluated first on controlled scenarios, where the differences with alternative metrics are evident. Then, the metrics have been applied to eight approaches of group detection, on eight public datasets, providing a fresh-new panorama of the state-of-the-art, discovering interesting strengths and pitfalls of the recent approaches. [ABSTRACT FROM AUTHOR]

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results. [ABSTRACT FROM AUTHOR]

With the prevalence of smart devices, such as smart phones, wearable equipments, and infrastructures, location-based service (LBS) has thrived in our daily life. In those practical LBS applications, group detection and tracking is a context-related research field in many scenarios, such as school yard, office building, shopping mall and so on. In this paper, we heuristically develop a temporal-spatial method for clustering and locating the groups, and then leverage a CRF-based event detection mechanism to improve the performance of recognizing contextual behaviors. The experimental results demonstrate that our system can achieve an impressive accuracy and precision of grouping and tracking.

In Computer Vision and Pattern Recognition, surveillance-video crowded scenes have been analysed according to their structure, where the detection of distinguishable people groups is an essential step. In this paper, we are interested in detecting F-Formations (i.e. free standing conversational groups) on video, which are formed by people social relations. We proposed a new method based on fuzzy relations, where a new social representation for computing relation between individuals, fusion for search consensus in multiple frame and clustering are introduced. Finally, our proposal was tested in a real-world dataset, improving the already reported scores from literature. [ABSTRACT FROM AUTHOR]

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function ($G$ -MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems. [ABSTRACT FROM PUBLISHER]

Detecting groups is becoming of relevant interest as an important step for scene (and especially activity) understanding. Differently from what is commonly assumed in the computer vision community, different types of groups do exist, and among these, standing conversational groups (a.k.a. F-formations) play an important role. An F-formation is a common type of people aggregation occurring when two or more persons sustain a social interaction, such as a chat at a cocktail party. Indeed, detecting and subsequently classifying such an interaction in images or videos is of considerable importance in many applicative contexts, like surveillance, social signal processing, social robotics or activity classification, to name a few. This paper presents a principled method to approach to this problem grounded upon the socio-psychological concept of an F-formation. More specifically, a game-theoretic framework is proposed, aimed at modeling the spatial structure characterizing F-formations. In other words, since F-formations are subject to geometrical configurations on how humans have to be mutually located and oriented, the proposed solution is able to account for these constraints while also statistically modeling the uncertainty associated with the position and orientation of the engaged persons. Moreover, taking advantage of video data, it is also able to integrate temporal information over multiple frames utilizing the recent notions from multi-payoff evolutionary game theory. The experiments have been performed on several benchmark datasets, consistently showing the superiority of the proposed approach over the state of the art, and its robustness under severe noise conditions. [ABSTRACT FROM AUTHOR]

A low complexity decoder was presented applicable to time-selective fading channels based on the existed group layered space-time （GLST） architecture. The intra symbol interferences within the all groups were firstly sup-pressed in virtue of the construction of orthogonal matrices and then obtained the all groups’ transmitted symbols based on the group detection technique using the simplified matrix inverse algorithm. The computational complexities of the proposed decoder were decreased greatly compared with the existed conversational decoding scheme. The simulation re-sults also show that the two kinds of decoders obtain the almost same performance at the minimum receiver antenna number under various time variations of channels and group number. At the same time, the performances of the two kinds of decoders get worse as the increase of time variations of channels or the group number but the performance loss can be compensated by the increase of receive antenna number.