Multi view image surveillance and tracking

Black, J.; Ellis, Tim; Rosin, Paul L.

doi:10.1109/motion.2002.1182230

Cited by 138 publications

(75 citation statements)

References 80 publications

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“…These early approaches to data association usually relied on gating and Kalman filtering, which have later made their way into our community [6,19]. Because of their recursive nature, they are prone to errors that are difficult to recover from by using a post processing step.…”

Section: Related Workmentioning

confidence: 99%

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Tracking Interacting Objects Optimally Using Integer Programming

Wang

Türetken

Fleuret

et al. 2014

Computer Vision – ECCV 2014

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Abstract. In this paper, we show that tracking different kinds of interacting objects can be formulated as a network-flow Mixed Integer Program. This is made possible by tracking all objects simultaneously and expressing the fact that one object can appear or disappear at locations where another is in terms of linear flow constraints. We demonstrate the power of our approach on scenes involving cars and pedestrians, bags being carried and dropped by people, and balls being passed from one player to the next in a basketball game. In particular, we show that by estimating jointly and globally the trajectories of different types of objects, the presence of the ones which were not initially detected based solely on image evidence can be inferred from the detections of the others.

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Section: Related Workmentioning

confidence: 99%

“…This can be done recursively [6,19], using dynamic programming [26,11,23], or using Linear Programming [25,15,5].…”

Section: Introductionmentioning

confidence: 99%

Tracking Interacting Objects Optimally Using Integer Programming

Wang

Türetken

Fleuret

et al. 2014

Computer Vision – ECCV 2014

View full text Add to dashboard Cite

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“…According to (2), H¡. spatial position could be identified with the mínimum volume Vk that contains Hk with probability greater or equal to P¡j, given that Hk is present in the scene (where, evidently 0 < PH < 1).…”

Section: State-space Models For 3d Segmentation and Trackingmentioning

confidence: 99%

“…A frequent approach for tracking múltiple people in multi-camera environments assumes a ground plañe restriction: interest objects move on a visible plañe (ground), making it possible to establish homographies relating different views [2], and then combining different trajectories onto the ground plañe. Although this method shows effective in several situations, ground plañe restriction does not hold for many interesting environments (specially for indoor scenarios).…”

Section: Introductionmentioning

confidence: 99%

3D Tracking Using Multi-view Based Particle Filters

Mohedano

García

Salgado

et al. 2008

Advanced Concepts for Intelligent Vision Systems

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Abstract. Visual surveillance and monitoring of indoor environments using múltiple cameras has become a field of great activity in computer visión. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difñculties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overeóme this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from múltiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estímate the probability of a certain volume being oceupied by a moving object, and thus to segment and track múltiple people across the monitored área. The proposed method is developed on the basis of simple, binary 2D moving región segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a náive color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios.

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“…Much of previous research has focused on determining the trajectory of objects in single camera views [1], [2]. More recently, effort has focused on robustly tracking targets through complex environments viewed by multiple cameras [3], addressing the problems of combining corresponding information where the FOVs are overlapped.…”

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confidence: 99%

Learning Semantic Scene Models From Observing Activity in Visual Surveillance

Makris

Ellis

2005

IEEE Trans. Syst., Man, Cybern. B

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226

164

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Abstract-This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a visual surveillance environment.

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Multi view image surveillance and tracking

Abstract: Abstract

Cited by 138 publications

References 80 publications

Tracking Interacting Objects Optimally Using Integer Programming

Tracking Interacting Objects Optimally Using Integer Programming

3D Tracking Using Multi-view Based Particle Filters

Learning Semantic Scene Models From Observing Activity in Visual Surveillance

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