Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection

Yilmaz, Alper

doi:10.1109/cvpr.2007.382987

Cited by 163 publications

(96 citation statements)

References 11 publications

(18 reference statements)

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“…The object tracking literature contains a wide variety of well-established algorithms and extensions of those algorithms. For example, the mean-shift tracker [7,8] has been the basis of numerous extensions [15,28,54]. The interested reader is referred to the review by Yilmaz et al [55] for a good treatment of the state-of-the-art in object tracking.…”

Section: Chapter 2 Related Workmentioning

confidence: 99%

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

Tung

Zelek

Clausi

2011

Image and Vision Computing

109

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Video surveillance systems are playing an increasing role in preventing and investigating crime, protecting public safety, and safeguarding national security. In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors like human fatigue and boredom.The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features, this thesis improves a recently introduced approach that makes use of higher-level features of intentionality. Individuals in a scene are modelled as intentional agents instead of simply objects. Unusual behaviour detection then becomes a task of determining whether an agent's trajectory is explicable in terms of learned spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.iii

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Section: Chapter 2 Related Workmentioning

confidence: 99%

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

Tung

Zelek

Clausi

2011

Image and Vision Computing

109

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“…(12) shows that the mean shift iteration formula is invariant to the scale transformation of weights. Therefore, BWH actually does not enhance mean shift tracking by transforming the representation of target model and target candidate model.…”

Section: The Equivalence Of Bwh Representation To Usual Representationmentioning

confidence: 99%

Robust mean-shift tracking with corrected background-weighted histogram

et al. 2012

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Abstract:The background-weighted histogram (BWH) algorithm proposed in [2] attempts to reduce the interference of background in target localization in mean shift tracking. However, in this paper we prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, i.e. BWH does not introduce any new information because the mean shift iteration formula is invariant to the scale transformation of weights. We then propose a corrected BWH (CBWH) formula by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localization. The experimental results show that CBWH can lead to faster convergence and more accurate localization than the usual target representation in mean shift tracking. Even if the target is not well initialized, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.

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“…Borrowing terminology from automatic control, we consider the orientation to be a slowchanging variable, which, in turn, allows us to update it with one-frame lag without sacrificing the effectiveness of the approach. In this way, such a general, inertial temporal prior regularizes the dynamics of an object by favoring shift motion that is common during tracking [43].…”

Section: Approachmentioning

confidence: 99%

“…Owing to its simplicity, robustness, and speed, it has been popular and has evolved over the years [7,14,24]. In particular, [43] represents an elongated, rigid object by an asymmetric kernel and determines its location, scale, and orientation. However, these algorithms search locally (except [39]) requiring objects to move slowly.…”

Section: Related Workmentioning

confidence: 99%

Pose-Configurable Generic Tracking of Elongated Objects

Węsierski

Horain

2013

2013 IEEE International Conference on Computer Vision

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Elongated objects have various shapes and can shift, rotate, change scale, and be rigid or deform by flexing, articulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging.We describe a unified, configurable framework for tracking the pose of elongated objects, which move in the image plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of multiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances.While the trend in tracking is to design complex, structure-free algorithms that update object appearance online, we show that our tracker, with the novel but remarkably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art performance. Most importantly, our model can be easily configured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.

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Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection

Cited by 163 publications

References 11 publications

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

Robust mean-shift tracking with corrected background-weighted histogram

Pose-Configurable Generic Tracking of Elongated Objects

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