A Fast Model-Free Morphology-Based Object Tracking Algorithm

Owens, J.; Hunter, Andrew; Fletcher, E.

doi:10.5244/c.16.75

Cited by 26 publications

(27 citation statements)

References 12 publications

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“…The weakness of such method is in the separation process of the person from the background when they have a same limiting the background and Kalman filer color cloth with the background [5]. Tracking by: In this method, bubbles achieved after elimination process of the background are used as signs for the object presence.…”

Section: Examples and Exprimentationmentioning

confidence: 99%

An Adaptive Mesh Method for Object Tracking

Koohi¹,

Shakery²

2011

IJP2P

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Section: Examples and Exprimentationmentioning

confidence: 99%

An Adaptive Mesh Method for Object Tracking

Koohi¹,

Shakery²

2011

IJP2P

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“…The system includes a robust tracker that is capable of handling partial occlusion [26]. The system makes use of all available visual information to successfully track moving objects.…”

Section: B Object Trackingmentioning

confidence: 99%

Autonomous real-time surveillance system with distributed IP cameras

Appiah

Hunter

Owens

et al. 2009

2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)

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Abstract-An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator.

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“…o Feature-based algorithms rely on two modules performing the extraction of elements and their segmentation through a clustering process; this way more than one feature can be identified in each relevant area and the features can be tracked independently until their centroid movements have distinguishable velocity (even in case of occlusions). o The last group is called model-based [14] and relies on the attempt of finding a match between the identified object and a model, based on a prior knowledge. Due to the envisaged application, we restrict our description to human models and tracking.…”

Section: Object Trackingmentioning

confidence: 99%

Security and safety assessment for cultural heritage: enhancing security profiles by wireless video-surveillance

Mondini¹,

Olivero²,

Scopigno³

et al. 2007

WIT Transactions on the Built Environment, Vol 94

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Museums, monuments, picture galleries and works of art represent major assets that need security and safety measures against both anthropic (thefts, vandalism, terrorism) and environmental threats. On behalf of the Compagnia di San Paolo, SiTI designed an assessment tool to score security and safety levels of cultural/artistic site. The project, called "Security and Safety Assessment of Cultural Heritage" (SESASS), provides a "protocol" to evaluate security and safety profiles through a process involving checklists in all relevant areas.The lack of a robust video-surveillance system clearly emerged as the most frequent weakness of a number of assessments: as a fact, the ability to recognize (and classify) objects, persons, human activities and behaviours "automatically", managing alarms criteria, is fundamental to (help) perform security tasks in a scalable way, overcoming typical limitations of a "pure human" approach. For this reason SiTI and ISMB decided to jointly focus on video-surveillance of cultural heritage, taking into account both technological aspects and organisational requirements (operative procedures). In this paper both the available technological solutions and the involved organizational and legal aspects are shortly overviewed.

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A Fast Model-Free Morphology-Based Object Tracking Algorithm

Cited by 26 publications

References 12 publications

An Adaptive Mesh Method for Object Tracking

An Adaptive Mesh Method for Object Tracking

Autonomous real-time surveillance system with distributed IP cameras

Security and safety assessment for cultural heritage: enhancing security profiles by wireless video-surveillance

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