Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, large-number abnormal behavior, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormal behaviors. In this paper, our contribution is twofold. First, we introduce an annotated and labeled large-scale crowd abnormal behaviors Hajj dataset (HAJJv2). Second, we propose two methods of hybrid Convolutional Neural Networks (CNNs) and Random Forests (RFs) to detect and recognize Spatio-temporal abnormal behaviors in small and large-scales crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individuals detection method based on the magnitudes and orientations of Horn-Schunck optical flow is used to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means, variances, and standard deviations statistical features are computed and fed to the RF to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain.
Center of Research Excellence in Hajj and Umrah, Umm Al-Qura University（乌姆·库拉大学朝觐与副朝研究卓越中心）；College of Computers and Information Systems, Umm Al-Qura University（乌姆·库拉大学计算机与信息系统学院）
In this paper, a new automatic system for classifying ritual locations in diverse Hajj and Umrah video scenes is investigated. This challenging subject has mostly been ignored in the past due to several problems one of which is the lack of realistic annotated video datasets. HUER Dataset is defined to model six different Hajj and Umrah ritual locations[26]. The proposed Hajj and Umrah ritual location classifying system consists of four main phases: Preprocessing, segmentation, feature extraction, and location classification phases. The shot boundary detection and background/foregroud segmentat…
Center of Research Excellence in Hajj and Umrah (HajjCore)（朝觐与副朝卓越研究中心（HajjCore））；College of Computers and Information Systems（计算机与信息系统学院）；Umm Al-Qura University（乌姆·库拉大学）
This note describes an integrated recognition system for identifying missing and found objects as well as missing, dead, and found people during Hajj and Umrah seasons in the two Holy cities of Makkah and Madina in the Kingdom of Saudi Arabia. It is assumed that the total estimated number of pilgrims will reach 20 millions during the next decade. The ultimate goal of this system is to integrate facial recognition and object identification solutions into the Hajj and Umrah rituals. The missing and found computerized system is part of the CrowdSensing system for Hajj and Umrah crowd estimation, …
We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Aven…
Technology Innovation Center, Wadi Makkah（瓦迪麦加技术创新中心）；Science and Technology Unit, Umm Al Qura University（乌姆·库拉大学科学与技术处）
The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host millions of pilgrims every year. During Hajj, the movement of large number of people has a unique spatial and temporal constraints, which makes Hajj one of toughest challenges for crowd management. In this paper, we propose a computer vision based framework that automatically analyses video sequence and computes important measurements which include estimation of crowd density, identification of dominant patterns, detection and localization of congestion. In addition, we analyze helpful statistics of the crowd like speed, an…
In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the …
Shanghai Research Institute of ETstar information tech. co. ltd（上海星途信息技术有限公司上海研究院）
We present a work-flow which aims at capturing residents' abnormal activities through the passenger flow of elevator in multi-storey residence buildings. Camera and sensors (hall sensor, photoelectric sensor, gyro, accelerometer, barometer, and thermometer) with internet connection are mounted in elevator to collect image and data. Computer vision algorithms such as instance segmentation, multi-label recognition, embedding and clustering are applied to generalize passenger flow of elevator, i.e. how many people and what kinds of people get in and out of the elevator on each floor. More specifi…
The severity of sustained injury resulting from assault-related violence can be minimised by reducing detection time. However, it has been shown that human operators perform poorly at detecting events found in video footage when presented with simultaneous feeds. We utilise computer vision techniques to develop an automated method of abnormal crowd detection that can aid a human operator in the detection of violent behaviour. We observed that behaviour in city centre environments often occur in crowded areas, resulting in individual actions being occluded by other crowd members. We propose a r…
University of Genova（热那亚大学）；University of Trento（特伦托大学）；SAP SE（思爱普公司）；Istituto Italiano di Tecnologia（意大利理工学院）；Polytechnique Montréal（蒙特利尔综合理工学院）
Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from ex…
University of Genova（热那亚大学）；SAP SE.（思爱普公司）；University of Trento（特伦托大学）
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality…
Hakim Sabzevari University（哈基姆萨布泽瓦里大学）；DISI, University of Trento（特伦托大学信息工程与计算机科学系）
In crowd behavior understanding, a model of crowd behavior need to be trained using the information extracted from video sequences. Since there is no ground-truth available in crowd datasets except the crowd behavior labels, most of the methods proposed so far are just based on low-level visual features. However, there is a huge semantic gap between low-level motion/appearance features and high-level concept of crowd behaviors. In this paper we propose an attribute-based strategy to alleviate this problem. While similar strategies have been recently adopted for object and action recognition, a…
Center for Interdisciplinary Information Science Research, ZhengZhou University（郑州大学交叉信息科学研究中心）
This paper proposes a method based on repulsive forces and sparse reconstruction for the detection and location of abnormal events in crowded scenes. In order to avoid the challenging problem of accurately tracking each specific individual in a dense or complex scene, we divide each frame of the surveillance video into a fixed number of grids and select a single representative point in each grid as the individual to track. The repulsive force model, which can accurately reflect interactive behaviors of crowds, is used to calculate the interactive forces between grid particles in crowded scenes…
Xidian University（西安电子科技大学）；Sea AI Lab；Nanyang Technological University（南洋理工大学）
Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the nor…
In recent years, anomaly events detection in crowd scenes attracts many researchers' attention, because of its importance to public safety. Existing methods usually exploit visual information to analyze whether any abnormal events have occurred due to only visual sensors are generally equipped in public places. However, when an abnormal event in crowds occurs, sound information may be discriminative to assist the crowd analysis system to determine whether there is an abnormality. Compare with vision information that is easily occluded, audio signals have a certain degree of penetration. Thus, …
Beihang University（北京航空航天大学）；Dalian Maritime University（大连海事大学）；University of Technology of Troyes（特鲁瓦工程技术大学）
It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised …
Fudan University（复旦大学）；China University of Petroleum（中国石油大学）
In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult. We associate the optical flows of multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor referred to as shape contexts to describe such short-term trajectories. Furthermore, we propose a K-NN similarity-based statistical model to detect anomalies over time and space, which is an unsupervised one-class learning algorithm requiring no clustering nor any prior assumption. Firstly, we…
From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Peking University（北京大学）；Tencent Youtu Lab（腾讯优图实验室）
Insight Centre for Data Analytics（数据分析洞察中心）；Dublin City University（都柏林城市大学）
This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness [1] and crowd conflict [2], with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abn…
SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neural Networks
School of Microelectronics（微电子学院）；School of Electrical and Information Engineering（电气与信息工程学院）；Optoelectronic Thin Film Device and Technology Research Institute（光电薄膜器件与技术研究所）
Transformer-Driven Multimodal Fusion for Explainable Suspiciousness Estimation in Visual Surveillance
Big Data Research and Supercomputing Division, CSIR Fourth Paradigm Institute（CSIR第四范式研究所大数据研究与超级计算部门）；Department of Electrical Engineering, Bharti School of Telecommunication, Yardi School of Artificial Intelligence, IIT Delhi（电信学院电子工程系、Yardi人工智能学院、德里理工学院）
Currently, the safety of people has become a very important problem in different places including subway station, universities, colleges, airport, shopping mall and square, city squares. Therefore, considering intelligence event detection systems is more and urgently required. The event detection method is developed to identify abnormal behavior intelligently, so public can take action as soon as possible to prevent unwanted activities. The problem is very challenging due to high crowd density in different areas. One of these issues is occlusion due to which individual tracking and analysis be…
In recent years, crowd analysis is important for applications such as smart cities, intelligent transportation system, customer behavior prediction, and visual surveillance. Understanding the characteristics of the individual motion in a crowd can be beneficial for social event detection and abnormal detection, but it has rarely been studied. In this paper, we focus on the extraversion measure of individual motions in crowds based on trajectory data. Extraversion is one of typical personalities that is often observed in human crowd behaviors and it can reflect not only the characteristics of t…
University of Genova（热那亚大学）；University of Trento（特伦托大学）；Carlos III University of Madrid（马德里卡洛斯三世大学）
In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging a…
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工学院））
Abnormal event detection (AED) in urban surveillance videos has multiple challenges. Unlike other computer vision problems, the AED is not solely dependent on the content of frames. It also depends on the appearance of the objects and their movements in the scene. Various methods have been proposed to address the AED problem. Among those, deep learning based methods show the best results. This paper is based on deep learning methods and provides an effective way to detect and locate abnormal events in videos by handling spatio temporal data. This paper uses generative adversarial networks (GAN…
University of Twente（特文特大学）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Zhejiang University（浙江大学）
In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on opt…
