Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning

Wearing masks is an effective and simple method to prevent the spread of the COVID-19 pandemic in public places, such as train stations, classrooms, and streets. It is of positive significance to urge people to wear masks with computer vision technology. However, the existing detection methods are mainly for simple scenes, and facial missing detection is prone to occur in dense crowds with different scales and occlusions. Moreover, the data obtained by surveillance cameras in public places are difficult to be collected for centralized training, due to the privacy of individuals. In order to solve these problems, a cascaded network is proposed: the first level is the Dilation RetinaNet Face Location (DRFL) Network, which contains Enhanced Receptive Field Context (ERFC) module with the dilation convolution, aiming to reduce network parameters and locate faces of different scales. In order to adapt to embedded camera devices, the second level is the SRNet20 network, which is created by Neural Architecture Search (NAS). Due to privacy protection, it is difficult for surveillance video to share in practice, so our SRNet20 network is trained in federated learning. Meanwhile, we have made a masked face dataset containing about 20,000 images. Finally, the experiments highlight that the detection mAP of the face location is 90.6% on the Wider Face dataset, and the classification mAP of the masked face classification is 98.5% on the dataset we made, which means our cascaded network can detect masked faces in dense crowd scenes well.

Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning

Finding a feasible, collision-free path in line with social activities is an important and challenging task for robots working in dense crowds. In recent years, many studies have used deep reinforcement learning techniques to solve this problem. In particular, it is necessary to find an efficient path in a short time which often requires predicting the interaction with neighboring agents. However, as the crowd grows and the scene becomes more and more complex, researchers usually simplify the problem to a one-way human-robot interaction problem. But, in fact, we have to consider not only the interaction between humans and robots but also the influence of human-human interactions on the movement trajectory of the robot. Therefore, this article proposes a method based on deep reinforcement learning to enable the robot to avoid obstacles in the crowd and navigate smoothly from the starting point to the target point. We use a dual social attention mechanism to jointly model human-robot and human-human interaction. All sorts of experiments demonstrate that our model can make robots navigate in dense crowds more efficiently compared with other algorithms.

Safety Concerns Emerging from Robots Navigating in Crowded Pedestrian Areas

The slogan “robots will pervade our environment” has become a reality. Drones and ground robots are used for commercial purposes while semi-autonomous driving systems are standard accessories to traditional cars. However, while our eyes have been riveted on dangers and accidents arising from drones falling and autonomous cars’ crashing, much less attention has been ported to dangers arising from the imminent arrival of robots that share the floor with pedestrians and will mix with human crowds. These robots range from semi or autonomous mobile platforms designed for providing several kinds of service, such as assistant, patrolling, tour-guide, delivery, human transportation, etc. We highlight and discuss potential sources of injury emerging from contacts of robots with pedestrians through a set of case studies. We look specifically at dangers deriving from robots moving in dense crowds. In such situations, contact will not only be unavoidable, but may be desirable to ensure that the robot moves with the flow. As an outlook toward the future, we also offer some thoughts on the psychological risks, beyond the physical hazards, arising from the robot’s appearance and behaviour. We also advocate for new policies to regulate mobile robots traffic and enforce proper end user’s training.

Advanced models for centroidal particle dynamics: short-range collision avoidance in dense crowds

Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with typically sparse pedestrian scenarios. Centroidal particle dynamics (CPD) is a method we defined that explicitly models the compressible personal space area surrounding each entity to inform its local pathing and collision-avoidance decisions. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level with higher congruence to real trajectory data and with more visually convincing collision-avoidance paths than the existing state of the art. We present advanced models in which we consider distraction of the pedestrians in the crowd, flocking behavior, interaction with vehicles (ambulances, police) and other advanced models that show that emergent behavior in the simulated crowds is similar to the behavior observed in reality. We discuss how to increase confidence in CPD, potentially making it also suitable for use in safety-critical applications, including urban design, evacuation analysis, and crowd-safety planning.

Estimating density limits for walking pedestrians keeping a safe interpersonal distancing

With people trying to keep a safe distance from others due to the COVID-19 outbreak, the way in which pedestrians walk has completely changed since the pandemic broke out1,2. In this work, laboratory experiments demonstrate the effect of several variables—such as the pedestrian density, the walking speed and the prescribed safety distance—on the interpersonal distance established when people move within relatively dense crowds. Notably, we observe that the density should not be higher than 0.16 pedestrians per square meter (around 6 m2 per pedestrian) in order to guarantee an interpersonal distance of 1 m. Although the extrapolation of our findings to other more realistic scenarios is not straightforward, they can be used as a first approach to establish density restrictions in urban and architectonic spaces based on scientific evidence.

Guardrail-based air supply terminal for improving ventilation effectiveness and saving energy in the waiting zone

In the waiting zones of airports, train stations, and other transportation hubs, poor indoor environment is primarily caused by human body-related factors in conjunction with the high density of people. Existing ventilation systems cannot effectively remove the waste heat and pollutants generated by dense crowds. In this paper, a guardrail-based air supply terminal is proposed. Two indices, the velocity target value and temperature target value, were introduced to facilitate the evaluation of the guardrail-based air supply terminal. The jet air velocity, penetration air velocity and width of the unventilated strip are optimized based on CFD numerical simulations; the values obtained were V1 = 0.25 m/s, V2 = 0.15 m/s, and W = 290 mm. The guardrail-based air supply terminal was found to create a homogeneous air velocity of 0.3 m/s to avoid draft sensations. The uniformity and effectiveness of the air supply via the optimized guardrails are verified by full-scale experiments and visual experiments. The temperature of the working area was maintained at 26°C in the summer, creating a comfortable environment. Compared with other existing air distribution systems in high and large spaces, the velocity target value, air age, and temperature target value with the proposed air supply terminal were the smallest. The energy consumption of the guardrail-based air supply terminal was 61% less than that of the vertical wall jets. The results indicate that the guardrail-based air supply terminal not only meets the thermal comfort requirements but also saves energy.

Density-induced consensus protocol

The paper introduces a model of collective behavior where agents receive information only from sufficiently dense crowds in their immediate vicinity. The system is an asymmetric, density-induced version of the Cucker–Smale model with short-range interactions. We prove the basic mathematical properties of the system and concentrate on the presentation of interesting behaviors of the solutions. The results are illustrated by numerical simulations.