According to historical crowd accidents data, pedestrian panic behaviors have often triggered and further exacerbated crowd incidents, leading to severe casualties in transit hubs. To date, most existing panic behavior recognition models still fail to detect crowd panic quickly enough to guide timely pedestrian-control measures. To address this gap, we propose the pedestrian Panic Behavior Recognition Model (PBRM), which integrates audio-semantic signals, speech-derived panic keywords, 21-keypoint kinematic patterns associated with running and falls, and multi-inflection pedestrian trajectories. These heterogeneous cues are consolidated through a lightweight fusion mechanism—efficient enough for real-time execution on edge devices—to generate a unified panic score, thereby providing operators with a rapid and reliable basis for crowd-control decisions. Evaluated on the four-hour Hongqiao-Panic dataset collected at Hongqiao Railway Station, PBRM achieves 84.2% precision, 85.7% recall, 84.9% F1, and 1.18 s average alert lead, improving the best single-modality baseline by 3.0 pp F1 while keeping the false-alarm rate (FAR) below 8%. Ablation study shows that audio-semantic channels dominate early cues, where as posture-trajectory streams reinforce decision robustness under occlusion. These results demonstrate that PBRM can be deployed on edge devices to provide operators with a critical lead time for crowd-control interventions, advancing intelligent transportation safety from post-event surveillance to proactive prevention.

In public transportation hubs, the combined motion (CM) between a passenger and his luggage is a primary movement form. The state of CM always influences the stability of the movement of passenger crowds. While there is still a lack of systematic studies on the passenger-luggage combined motion, even most state-of-the-art literatures focus only on the impact of luggage on the overall efficiency of crowd evacuation. To fill this academic lag, this study investigated the motion relationship between passengers and luggage and then proposed a passenger-luggage combined motion model (PLCMM), which helps determine the relevance between passengers and luggage. Furthermore, a novel energy-based criterion for the stability of PLCMM and an abnormal state recognition model were established. Finally, the proposed model and criteria were validated in the waiting hall of Shanghai Hongqiao High-speed Railway Station, as a typical large public transportation scenario. The luggage abnormality recognition model can identify three abnormal states, i.e., luggage falling off, tail dumping, and sudden acceleration. A performance comparison between the proposed PLCMM and five other representative models was conducted from the aspects of scenarios, algorithm complexity, and research focus. The detection accuracy of the PLCCM was 96.154% and the algorithm complexity was considerably lower than the complexity exhibited by the purely computer vision-based approaches. The results demonstrated that PLCMM aligned closely with the ground truth, and outperformed other state-of-the-art models. The stability criterion can be employed to assess the impact of lateral perturbation forces on the stability of the passenger-luggage combined motion, to support the daily passenger flow control and management in more public traffic areas.

Panic behaviors in a pedestrian flow often lead to a state of chaos or disorder among the pedestrian crowd, resulting in a crowd accident with high possibility. To investigate the panic pedestrian dynamics and further prevent serious crowd accidents, simulation based on dynamics modeling and accident video data is a popular solution to date. Thereby, it is challenging but significant to improve the crowd dynamics model more consistent with the ground truth of real pedestrian movement scenarios, with consideration of both physical contact force and panic emotional propagation in a crowd. Therefore, this study proposed an extended social force model (ESFM) by applying the physical contact-force estimation during pedestrian collision based on non-smooth contact dynamics. Subsequently, the ESFM was integrated with an improved panic propagation model (IPPM) considering obstacle and promotion factors. Finally, taking the crowd panic accident happened in Nepal in 2015 as an experiment case, the simulation of panic crowd dynamics was conducted within Anylogic software. Four cases of SFM, ESFM, SFM+IPPM, and ESFM+IPPM were compared quantitatively and graphically. The experimental results showed that the pedestrian distribution obtained from the proposed ESFM+IPPM was the closest to the ground truth during the panic response period, with 28.8% lower of Hausdorff distance than the original SFM, and 21.6% lower the well-known BHSFM, respectively. This approach can help improve the panic crowd modeling and pedestrian distribution prediction in real scenarios.

Based on collaborative representation, we present a novel semisupervised dimensionality reduction (DR) method termed collaborative label propagation-based semisupervised linear discriminant analysis (CLP-SLDA) for hyperspectral images (HSIs). The new method needs three steps to obtain the optimal projection vector. Firstly, CLP-SLDA utilizes the collaborative label propagation (CLP) technique to obtain the weak labels of the unlabeled samples and the confidence scores of the weak labels. Secondly, a novel weight matrix is constructed based on the known labels, newly acquired weak labels and confidence scores of the weak labels. Thirdly, the newly obtained weight matrix is utilized to learn the optimal transformation vectors to achieve DR for HSIs. Compared to some other state-of-the-art semisupervised DR methods, our proposed CLP-SLDA can acquire the highest classification accuracy in 9 of 13 classes for KSC and 4 of 9 classes for PU and achieve the best performance in AA, OA, and k.

A fall is an abnormal behavior that rarely occurs, however, once it occurs in a crowded scenario, it is prone to cause local disturbance, density-velocity fluctuations, and crowd instability even leading to a stampede accident. Currently, research on fall behavior mainly depends on single-point detection approach but lacks investigation on disturbing mechanism in a crowd flow. To address this issue, this paper first proposed a pressure dynamics model based on limb-level contact to analyze pedestrian behavioral characteristics. Then, based on the random Brownian motion, the disturbance distribution of fall behavior was determined theoretically. Using two parameters (a pressure coefficient and disturbance intensity), the pressure term of the fluid dynamics Aw-Rascle model was improved, and the internal disturbance propagation model (DPM) of fall behavior was established, as a primary contribution of this study. Further, to eliminate the disturbance propagation in a crowd, damping motion theory was employed. The disturbance elimination mechanism of abnormal behavior was discussed to suppress disturbance propagation caused by a pedestrian fall behavior. To validate the proposed model, both field experiment and numerical simulation were conducted respectively. The stability performance of the proposed DPM was evaluated with standard deviation (less than 0.1631) based on 50 repetitive experiments. Results showed that this study could help discover the disturbance propagation dynamics and elimination mechanisms of pedestrian fall behavior in a crowded scenario.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication system with extremely large-scale antenna arrays can achieve ultra-high spectral efficiency. However, the conventional far-field beam codebooks may be mismatched with the near-field spherical-wavefront channel caused by large array aperture, which results in severe performance loss. To address this problem, we develop a criterion of code book design to maximize the worst-case beam gain within the beam coverage. Then, a closed-form expression of the near-field full dimensional (FD) codebook with non-orthogonal structure is derived, which can realize the spatial oversampling regardless of the number of antennas at the transceiver. Simulation results show that our proposed non-orthogonal codebook can potentially improve the accuracy of near-field beam training, compared with existing codebooks.

Convolutional networks have been widely used for the classification of hyperspectral images; however, such networks are notorious for their large number of trainable parameters and high computational complexity. Additionally, traditional convolution-based methods are typically implemented as a simple cascade of a number of convolutions using a single-scale convolution kernel. In contrast, a lightweight multiscale convolutional network is proposed, capitalizing on feature extraction at multiple scales in parallel branches followed by feature fusion. In this approach, 2D depthwise convolution is used instead of conventional convolution in order to reduce network complexity without sacrificing classification accuracy. Furthermore, multiscale channel attention is also employed to selectively exploit discriminative capability across various channels. To do so, multiple 1D convolutions with varying kernel sizes provide channel attention at multiple scales, again with the goal of minimizing network complexity. Experimental results reveal that the proposed network not only outperforms other competing lightweight classifiers in terms of classification accuracy but also exhibits a lower number of parameters as well as significantly less computational cost.

With the increasing number of video surveillance cameras in public buildings, it has become challenging, yet significant to detect abnormal pedestrian behaviors in crowd management, to prevent crowd accidents. Although current advancements in human action recognition based on computer vision can help detect abnormal behaviors after their incidence, majority of them lack the ability to detect potential characteristics prior to the occurrence of real abnormal behaviors. Hence, in this study, we addressed this issue by proposing a novel dynamic centroid model (DCM) of a human body, and rebuilding pedestrian joint sub-segments from human skeleton key nodes obtained in camera images. We built a weighted centroid-combined force model based on Newton’s second law, considering acceleration, mass inertial of human body sub-segments, and internal constraints. Thereafter, pedestrian kinematic and dynamic parameters were analyzed, such as speed, trajectory, force. Furthermore, abnormal behavior detection criteria were constructed for typical abnormal-behavior cases: U-turn and fall-down. Comparative experiments between the proposed DCM and the state-of-the-art methods were conducted. The experimental results showed that the model was capable of detecting abnormal behaviors, with mean values of lead time of 277 ms in U-turn behavior, and 562 ms in fall-down behavior, prior to the captured occurrence of these two abnormal behaviors. Finally, a de-occlusion algorithm was designed and jointly used with DCM, validated by a fall-down detecting experiment including partial occlusion. Therefore, this study holds significant value for the prevention of abnormal pedestrian behaviors in public places.

Remote sensing image (RSI) scene classification plays an active role in many application areas. Due to the excellent performance of the convolutional neural networks (CNNs), which have widely applied in RSI scene classification in recent years. However, most existing methods improve the classification accuracy by improving the model parameters or fusing the features of CNNs. This will make the whole model very complicated and unable to extract multiscale features at a more granular level. This letter proposes a novel and lightweight multiscale depthwise network (MSDWNet) with efficient spatial pyramid attention (ESPA), namely ESPA-MSDWNet, with low model parameters and high accuracy in solving this problem. The ESPA-MSDWNet uses MobileNet V2 as a backbone. We represent multiscale features at a more granular level and expand the receptive fields by multiscale depthwise convolution (MSDW Conv). We also propose the ESPA module to extract dependencies between channels. The ablation experiment verifies the effectiveness of our proposed MSDW Conv and ESPA module. Experimental results on three public RSI datasets show that ESPA-MSDWNet has advantages in classification accuracy and execution efficiency over current state-of-the-art (SOTA) methods.

In remote sensing scene classification (RSSC), features can be extracted with different spatial frequencies where high-frequency features usually represent detailed information and low-frequency features usually represent global structures. However, it is challenging to extract meaningful semantic information for RSSC tasks by just utilizing high- or low-frequency features. The spatial composition of remote sensing images (RSIs) is more complex than that of natural images, and the scales of objects vary significantly. In this article, a multiscale feature fusion covariance network (MF2CNet) with octave convolution (Oct Conv) is proposed, which can extract multifrequency and multiscale features from RSIs. First, the multifrequency feature extraction (MFE) module is used to obtain fine-grained frequency features by Oct Conv. Then, the features of different layers in MF2CNet are fused by the multiscale feature fusion (MF2) module. Finally, instead of using global average pooling (GAP), global covariance pooling (GCP) extracts high-order information from RSIs to capture richer statistics of deep features. In the proposed MF2CNet, the obtained multifrequency and multiscale features can effectively improve the performance of CNNs. Experimental results on four public RSI datasets show that MF2CNet has advantages in RSSC over current state-of-the-art methods. The source codes of this method can be found athttps://github.com/liuqingxin-chd/MF2CNet.

Crowd stability analysis is an important yet challenging task, as it is difficult to obtain the quantitative information regarding a crowd in motion, for instance, the crowd count and crowd density in a pedestrian merging area. This paper proposes a novel model that can be used to accurately analyze the crowd stability based on images obtained from a real-time video surveillance system (VSS) in dense crowd scenarios. To enhance the accuracy of the human head recognition for the crowd counting and crowd density estimation, we improve the conventional convolution-neural-network (CNN) model with more columns and merged features, obtaining a four-column convolutional neural network (4C-CNN). Using more columns with receptive fields of deferent sizes, more merged features can be learned, to be adaptive to variations in pedestrian head size due to image resolution. Furthermore, the crowd density of different areas is calculated with image rectification against the perspective distortion. By utilizing the stability criterion based on crowd density, we propose a crowd stability analysis model (CSAM) with the capability of quantitative computation dynamically. The results of extensive experiments performed on public datasets indicate that this improved CNN model exhibits a better performance for crowd counting than the typical multi-column CNN models. In addition, the experiment results pertaining to Shanghai Hongqiao Railway Station demonstrate the effectiveness of the crowd stability analysis model. Thus, this integrated approach can simultaneously conduct the processes of crowd counting, image rectification, density map calculation and crowd stability analysis by using dense crowd images from a VSS.

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m–2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m–1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54% under this epidemic.

As a classical supervised dimensionality reduction (DR) method, linear discriminant analysis (LDA) has been developed for many variants. However, it is not applicable to the case that labeled samples are scarce and unlabeled samples are in large quantity, which always happens in the real world. In this letter, we propose a novel technique format termed semisupervised LDA based on pairwise constraint propagation (SLDA-PCP) for hyperspectral images (HSIs). The basic idea of this method is to use a specially designed PCP technique to propagate label information from the labeled samples to the unlabeled samples. In addition, an extended LDA format to learn the optimal projection vectors according to the newly obtained label information is also created. Comprehensive experiments on two HSIs show that our SLDA-PCP performs better than some state-of-the-art semisupervised DR methods.

In emergencies close to a T-shaped street junction (TSJ), pedestrian overcrowding and trampling are typical unstable situations triggered by external disturbances, potentially leading to serious casualties during crowd movement. It is significant to analyze the pedestrian flow stability to achieve safe and efficient crowd evacuation. To analyze the stability of crowd flow, this study uses the two-dimensional Aw-Rascle dynamic-flow model to build the pedestrian merging dynamics at a TSJ area. Then the principle of the Lyapunov stability criterion in modern control theory is employed to analyze the stability of the pedestrian crowd. A novel stability criterion for an asymmetric pedestrian merging layout at a TSJ is then proposed. When the crowd acceleration in the local area exceeds the critical acceleration, the crowd will evolve to an unstable state. To validate this criterion, numerical simulations of a case study of a crowd stampede with serious casualties (1703 victims reported) occurring at a TSJ area in Mecca, Saudi Arabia, during the Hajj in 2015 is implemented. To build stable acceleration regimes, this paper tunes the key parameters of the number of pedestrians, width ratio of main street to branch street, and pedestrian drop-off location along the street 223, and discusses the stable ranges of acceleration to satisfy the pedestrian stability criterion. This criterion and the experimental results can supply new information to control pedestrian flow and keep it stable.

This study investigates a dynamic flow model for crowd evacuation at T-shaped street junctions (TSJs) from a macroscopic view. The Aw-Rascle traffic flow model is modified by constructing an impact matrix in the street intersection area to practically describe the crowd convergence mechanism at a TSJ. For coherence, this modified model is proved to be anisotropic, similar to the original Aw-Rascle traffic flow model. To describe real scenarios with higher crowd density and lower speeds during organized pilgrimages, the initial Gaussian distribution of the crowd is improved to a higher-order smoothing function. To validate the modified Aw-Rascle traffic flow model, we reconstruct the drastic stampede that occurred at the TSJ of streets 204 and 223 during the 2015 Mecca pilgrimage. Further, the main environmental parameters that potentially lead to a stampede are discussed with numerical simulations. A valuable suggestion is that the street width ratio should be extended from 1.1 to 1.4 to prevent stampedes, matching the expansion engineering of street 204 reported by BBC News. An interesting phenomenon is that the closer the bus unloading location on street 223 is to the TSJ center, the lower the maximum crowd density and the safer the pedestrians will be. With this modified Aw-Rascle flow model at TSJs, this paper provides strategic and technical suggestions for future crowd flow control to reduce the risk of crowd stampedes.

It is significant to discovery the impact of panic on crowd movement and study the panic propagation mechanism which can help real crowd control. This paper focuses on panic propagation dynamics in a high-density crowd based on information entropy theory and Aw-Rascle model. A novel concept of panic entropy is defined to quantify the confusion degree of crowd movement based on information entropy theory. The behavior characteristics are measured by the modulus and direction of the gridded crowd velocity vector in a two-dimension space. In order to study panic propagation dynamics, Aw-Rascle model is used to describe the crowd flow dynamics. Then, the dynamic model of panic propagation is proposed based on crowd flow. To validate the panic propagation model, numerical simulations are conducted based on one of stampedes happened in the Mecca Hajj in 2015. Simulation results show the relationship between the crowd panic entropy, density and velocity with visual panic distribution and different color contour diagrams. As a discussion result of the relationship between velocity and panic entropy, it is verified that: 1) moderate panic can help the crowd to keep relatively high velocity; 2) excessive panic can lead to irregular crowd movement and decrease crowd movement velocity especially in the over-crowded situation. The contribution of this paper is to provide a novel approach to quantify crowd panic degree and study panic propagation dynamics in a high-density crowd.

At the request for quick measurement of airborne lidar, the high-density data collected from selected areas was reduced to different percentage of its original density. And the digital elevation models (DEMs) was generated from the ground points extracting from varying point density data sets. Then the correlation between DEMs generated from lower resolution ground data and those from original ground data were analyzed and compared. It is found that point density data set of at least 0.6 points per square meter is necessary to generate an accurate digital elevation model for the test of the measured urban environment. The conclusion is important for quick data acquirement and quick scanning of disaster area after the occurrence of natural disasters.