40 research outputs found
Towards understanding of human behaviour in crowded spaces
Human behaviour in the real world is important information for developing human behaviour models and simulations. However, it is difficult to capture ‘real’ human behaviour since each human has unique char-acteristics. As part of the AUNT-SUE (Accessibility and User Needs in Transports - Sustainable Urban Environments) project, this research is aimed at understanding individual human behaviour in crowded spaces based on video observation analysis. The video observation analysis employed a video observation method where a multi-mode transportation system in Malaysia was selected as a case study. The observa-tion focus was at an exit door where considerable variety of human movement and behaviour could be observed. Six hours of video recording was conducted covering weekdays, weekends, peak and off-peak times. Almost 19,000 individual humans were observed and categorised into six different behaviours that were determined from the three major human movements of free, opposite direction and same direction movement
Wavelet-based Texture Model for Crowd Dynamic Analysis
Crowd event detection techniques aim at solving
real-world surveillance problems, such as detecting crowd
anomaly and tracking specific person in a highly dynamic
crowd scene. In this paper, we proposed an innovate
texture-based analysis method to model crowd dynamics
and us it to distinguish the crowd behaviours. To describe
complicated crowd scenes, homogeneous random features
have been deployed in the research for behavioural template
matching. Experiment results have shown that the anomaly
appearing in crowd scenes can be effectively and efficiently
identified by using the devised methods
Comparative Study of Various Crowd Detection and Classification Methods for Safety Control System
A crowd is a distinct collection of people or anything that is involved in community or society. The phenomenon of a crowd is fairly well known in a wide range of academic fields, including sociology, civil engineering, and physics, amongst others. At this point in time, it has developed into the most active-oriented research and fashionable issue in the field of computer vision. Pre-processing, object detection, and event or behavior identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. Pre-processing, object detection, and event or behaviour identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. This study gives a model of crowd analysis as well as a taxonomy of the most prevalent method to crowd analysis. It may be helpful to researchers and would serve as a good introduction connected to the area of work that has been conducted
Abnormal crowd behavior detection using novel optical flow-based features
In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The
proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and
direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample
deviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are
many optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature.
Evaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods
Crowd Saliency Detection via Global Similarity Structure
It is common for CCTV operators to overlook inter- esting events taking place
within the crowd due to large number of people in the crowded scene (i.e.
marathon, rally). Thus, there is a dire need to automate the detection of
salient crowd regions acquiring immediate attention for a more effective and
proactive surveillance. This paper proposes a novel framework to identify and
localize salient regions in a crowd scene, by transforming low-level features
extracted from crowd motion field into a global similarity structure. The
global similarity structure representation allows the discovery of the
intrinsic manifold of the motion dynamics, which could not be captured by the
low-level representation. Ranking is then performed on the global similarity
structure to identify a set of extrema. The proposed approach is unsupervised
so learning stage is eliminated. Experimental results on public datasets
demonstrates the effectiveness of exploiting such extrema in identifying
salient regions in various crowd scenarios that exhibit crowding, local
irregular motion, and unique motion areas such as sources and sinks.Comment: Accepted in ICPR 2014 (Oral). Mei Kuan Lim and Ven Jyn Kok share
equal contribution
Filters for Wi-Fi Generated Crowd Movement Data
Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individual's movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system
Crowd Behavior Analysis and Classification using Graph Theoretic Approach
Surveillance systems are commonly used for security and monitoring. The need to automate these systems is well understood. To address this issue we introduce the Graph theoretic approach based Crowd Behavior Analysis and Classification System (GCBACS). The crowd behavior is observed based on the motion trajectories of the personnel in the crowd. Optical flow methods are used to obtain the streak lines and path lines of the crowd personnel trajectories. The streak flow is constructed based on the path and streak lines. The personnel and their respective potential vectors obtained from the streak flows are used to represent each frame as a graph. The frames of the surveillance videos are analyzed using graph theoretic approaches. The cumulative variation in all the frames is computed and a threshold based mechanism is used for classification and activity recognition. The experimental results discussed in the paper prove the efficiency and robustness of the proposed GCBACS for crowd behavior analysis and classification
Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to
classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD
descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising
advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies
Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to
classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD
descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising
advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies