9,331 research outputs found
Survey on Vision-based Path Prediction
Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.Comment: DAPI 201
A Neural System for Automated CCTV Surveillance
This paper overviews a new system, the “Owens
Tracker,” for automated identification of suspicious
pedestrian activity in a car-park.
Centralized CCTV systems relay multiple video streams
to a central point for monitoring by an operator. The
operator receives a continuous stream of information,
mostly related to normal activity, making it difficult to
maintain concentration at a sufficiently high level.
While it is difficult to place quantitative boundaries on
the number of scenes and time period over which
effective monitoring can be performed, Wallace and
Diffley [1] give some guidance, based on empirical and
anecdotal evidence, suggesting that the number of
cameras monitored by an operator be no greater than 16,
and that the period of effective monitoring may be as
low as 30 minutes before recuperation is required.
An intelligent video surveillance system should
therefore act as a filter, censuring inactive scenes and
scenes showing normal activity. By presenting the
operator only with unusual activity his/her attention is
effectively focussed, and the ratio of cameras to
operators can be increased.
The Owens Tracker learns to recognize environmentspecific
normal behaviour, and refers sequences of
unusual behaviour for operator attention. The system
was developed using standard low-resolution CCTV
cameras operating in the car-parks of Doxford Park
Industrial Estate (Sunderland, Tyne and Wear), and
targets unusual pedestrian behaviour.
The modus operandi of the system is to highlight
excursions from a learned model of normal behaviour in
the monitored scene. The system tracks objects and
extracts their centroids; behaviour is defined as the
trajectory traced by an object centroid; normality as the
trajectories typically encountered in the scene. The
essential stages in the system are: segmentation of
objects of interest; disambiguation and tracking of
multiple contacts, including the handling of occlusion
and noise, and successful tracking of objects that
“merge” during motion; identification of unusual
trajectories. These three stages are discussed in more
detail in the following sections, and the system
performance is then evaluated
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
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