2,046 research outputs found
Spatio-temporal Texture Modelling for Real-time Crowd Anomaly Detection
With the rapidly increasing demands from surveillance and security industries, crowd behaviour analysis has become one of the hotly pursued video event detection frontiers within the computer vision arena in recent years. This research has investigated innovative crowd behaviour detection approaches based on statistical crowd features extracted from video footages. In this paper, a new crowd video anomaly detection algorithm has been developed based on analysing the extracted spatio-temporal textures. The algorithm has been designed for real-time applications by deploying low-level statistical features and alleviating complicated machine learning and recognition processes. In the experiments, the system has been proven a valid solution for detecting anomaly behaviours without strong assumptions on the nature of crowds, for example, subjects and density. The developed prototype shows improved adaptability and efficiency against chosen benchmark systems
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
Crowd anomaly detection for automated video surveillance
Video-based crowd behaviour detection aims at tackling challenging problems such as automating and identifying changing crowd behaviours under complex real life situations. In this paper, real-time crowd anomaly detection algorithms have been investigated. Based on the spatio-temporal video volume concept, an innovative spatio-temporal texture model has been proposed in this research for its rich crowd pattern characteristics. Through extracting and integrating those crowd textures from surveillance recordings, a redundancy wavelet transformation-based feature space can be deployed for behavioural template matching. Experiment shows that the abnormality appearing in crowd scenes can be identified in a real-time fashion by the devised method. This new approach is envisaged to facilitate a wide spectrum of crowd analysis applications through automating current Closed-Circuit Television (CCTV)-based surveillance systems
Bio-inspired relevant interaction modelling in cognitive crowd management
Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause–effect relationships between user actions and changes in environment configurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an effective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledge-transfer phase, learned representations can be used, at different levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario , where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator. These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, on synthetic and also on real video sequences, in both the user-support and automatic modes
Automatic human behaviour anomaly detection in surveillance video
This thesis work focusses upon developing the capability to automatically evaluate
and detect anomalies in human behaviour from surveillance video. We work with
static monocular cameras in crowded urban surveillance scenarios, particularly air-
ports and commercial shopping areas. Typically a person is 100 to 200 pixels high
in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo-
ple at any given time. Our procedure evaluates human behaviour unobtrusively to
determine outlying behavioural events,
agging abnormal events to the operator.
In order to achieve automatic human behaviour anomaly detection we address
the challenge of interpreting behaviour within the context of the social and physical
environment. We develop and evaluate a process for measuring social connectivity
between individuals in a scene using motion and visual attention features. To do this
we use mutual information and Euclidean distance to build a social similarity matrix
which encodes the social connection strength between any two individuals. We de-
velop a second contextual basis which acts by segmenting a surveillance environment
into behaviourally homogeneous subregions which represent high tra c slow regions
and queuing areas. We model the heterogeneous scene in homogeneous subgroups
using both contextual elements. We bring the social contextual information, the
scene context, the motion, and visual attention features together to demonstrate
a novel human behaviour anomaly detection process which nds outlier behaviour
from a short sequence of video. The method, Nearest Neighbour Ranked Outlier
Clusters (NN-RCO), is based upon modelling behaviour as a time independent se-
quence of behaviour events, can be trained in advance or set upon a single sequence.
We nd that in a crowded scene the application of Mutual Information-based social
context permits the ability to prevent self-justifying groups and propagate anomalies
in a social network, granting a greater anomaly detection capability. Scene context
uniformly improves the detection of anomalies in all the datasets we test upon.
We additionally demonstrate that our work is applicable to other data domains.
We demonstrate upon the Automatic Identi cation Signal data in the maritime
domain. Our work is capable of identifying abnormal shipping behaviour using joint
motion dependency as analogous for social connectivity, and similarly segmenting
the shipping environment into homogeneous regions
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