A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance

Morris, Brendan; Trivedi, Mohan M.

doi:10.1109/tcsvt.2008.927109

Cited by 421 publications

(231 citation statements)

References 90 publications

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“…Similar representations have previously been proposed by other authors and have shown to be useful for detecting trajectory anomalies [11,9]. Following Definition 1, let us formalise the concept of a sub-trajectory:…”

Section: Definition 1 a Trajectory Is A Continuos Parameterised Curvmentioning

confidence: 99%

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Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

Laxhammar

Falkman

2012

IFIP Advances in Information and Communication Technology

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Abstract. Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local sub-trajectory anomalies. Third, they involve tuning of many parameters and may suffer from high false alarm rates. The main contribution of this paper is the proposal and discussion of the Sliding Window Local Outlier Conformal Anomaly Detector (SWLO-CAD), which is an algorithm for online detection of local sub-trajectory anomalies. It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate. Moreover, SWLO-CAD is based on Local outlier factor, which is a previously proposed outlier measure that is sensitive to local anomalies. Thus, SWLO-CAD has a unique set of properties that address the issues above.

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Section: Definition 1 a Trajectory Is A Continuos Parameterised Curvmentioning

confidence: 99%

“…A large number of algorithms based on learning of normal trajectory patterns have been proposed for this problem [11]. However, these algorithms typically suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases, i.e.…”

Section: Introductionmentioning

confidence: 99%

Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

Laxhammar

Falkman

2012

IFIP Advances in Information and Communication Technology

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“…However, there are other works that successfully overcome the existent problems by building adaptive learning systems that can be applied to various scenes. Extensive surveys of related research that investigate and make use of trajectory information for video surveillance systems have been published [1][2][3].…”

Section: Related Workmentioning

confidence: 99%

Anomalous Trajectory Detection in Surveillance Systems Using Pedestrian and Surrounding Information

Doan¹,

Kim²,

Vo³

et al. 2016

IEIE Transactions on Smart Processing and Computing

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Concurrently detected and annotated abnormal events can have a significant impact on surveillance systems. By considering the specific domain of pedestrian trajectories, this paper presents two main contributions. First, as introduced in much of the work on trajectory-based anomaly detection in the literature, only information about pedestrian paths, such as direction and speed, is considered. Differing from previous work, this paper proposes a framework that deals with additional types of trajectory-based anomalies. These abnormal events take places when a person enters prohibited areas. Those restricted regions are constructed by an online learning algorithm that uses surrounding information, including detected pedestrians and background scenes. Second, a simple data-boosting technique is introduced to overcome a lack of training data; such a problem particularly challenges all previous work, owing to the significantly low frequency of abnormal events. This technique only requires normal trajectories and fundamental information about scenes to increase the amount of training data for both normal and abnormal trajectories. With the increased amount of training data, the conventional abnormal trajectory classifier is able to achieve better prediction accuracy without falling into the over-fitting problem caused by complex learning models. Finally, the proposed framework (which annotates tracks that enter prohibited areas) and a conventional abnormal trajectory detector (using the data-boosting technique) are integrated to form a united detector. Such a detector deals with different types of anomalous trajectories in a hierarchical order. The experimental results show that all proposed detectors can effectively detect anomalous trajectories in the test phase.

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“…An event is defined as the occurrence of a specific action performed by a subject in a particular place during a particular time frame [12]. Events are detected for the purpose of security for two key reasons [13][14][15]: warning of known threatening events [14,15] and searching for events of interest after the event has taken place.…”

Section: Related Workmentioning

confidence: 99%

Event Detection on Motion Activities Using a Dynamic Grid

2015

J Inf Process Syst

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Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects.

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A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance

Cited by 421 publications

References 90 publications

Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

Anomalous Trajectory Detection in Surveillance Systems Using Pedestrian and Surrounding Information

Event Detection on Motion Activities Using a Dynamic Grid

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