Multi-class object tracking algorithm that handles fragmentation and grouping

Böse, B.; Wang, Xiaogang; Grimson, Eric

doi:10.1109/cvpr.2007.383175

Cited by 61 publications

(46 citation statements)

References 8 publications

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“…only rely on the localization output to link detection over time. This is the case in blob based approaches cited above [12,13], but also when multiple cameras are used [3]. When possible, this is a powerful approach which allows to integrate long term trajectory information in a lightweight manner since only state features are involved, and not images.…”

Section: Key Factors and Related Workmentioning

confidence: 99%

“…However, since blobs do not always correspond to single objects, splitting of blobs into several tracks and the merge of tracks into one blob occur. To handle this issue, reasoning about the object counts and their appearance can be used to identify single tracks through Bayesian networks or graph analysis [12,13]. For instance, Bose et al [13] proposed the fragmentation and grouping scheme to deal with these situations.…”

Section: Key Factors and Related Workmentioning

confidence: 99%

“…To handle this issue, reasoning about the object counts and their appearance can be used to identify single tracks through Bayesian networks or graph analysis [12,13]. For instance, Bose et al [13] proposed the fragmentation and grouping scheme to deal with these situations. However, these approaches can not be applied when objects are closer to the camera, as the occlusions become too complex.…”

Section: Key Factors and Related Workmentioning

confidence: 99%

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Multi-Person Bayesian Tracking with Multiple Cameras

Yao

Odobez

2009

Multi-Camera Networks

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Object tracking is an important task within the field of computer vision, which is driven by the need to detect interesting moving objects in order to analyze and recognize their behaviours and activities. However, tracking multiple object is a complex task due to a large number of issues number ranging from the different types of sensing set-up to the complexity of the object appearance and behaviours.In this chapter, we analyze some of the important issues to solve for multiple object tracking, reviewing briefly how they are addressed in the literature. We then present a state-of-the-art algorithm for the tracking of a variable number of 3D persons in a multi-camera setting with partial field-of-view overlap. The algorithm illustrates how in a Bayesian framework the raised issues can be formulated and handled. More specifically, the tracking problem relies on a joint multi-object state space formulation with individual object states defined in the 3D world. It involves several key features for efficient and reliable tracking like the definition of appropriate multiobject dynamics and a global multi-camera observation model based on color and foreground measurements, the use of the Reversible-Jump Markov Chain Monte Carlo (RJ-MCMC) framework for efficient optimization, the exploitation of powerful human detector outputs in the MCMC proposals to automatically initialize/update object tracks. Experimental results on challenging real-world tracking sequences and situations demonstrate the efficiency of such an approach.

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Section: Key Factors and Related Workmentioning

confidence: 99%

Section: Key Factors and Related Workmentioning

confidence: 99%

Section: Key Factors and Related Workmentioning

confidence: 99%

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Multi-Person Bayesian Tracking with Multiple Cameras

Yao

Odobez

2009

Multi-Camera Networks

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“…To handle these problems, we propose a realtime multiple objects tracking algorithm based on Bose et al's framework handling grouping and fragments [15]. As shown in Fig.…”

Section: Object Trackingmentioning

confidence: 99%

“…Bose et al proposed an object tracking method considering with fragmentation problem [15]. They defined a generic object model and proposed an object tracking algorithm using inference graph.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Emergency Response System with a Paramedic Mobile Robot

Jeong

Choi

Cho

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYDetecting emergency situation is very important to a surveillance system for people like elderly live alone. A vision-based emergency response system with a paramedic mobile robot is presented in this paper. The proposed system is consisted of a vision-based emergency detection system and a mobile robot as a paramedic. A vision-based emergency detection system detects emergency by tracking people and detecting their actions from image sequences acquired by single surveillance camera. In order to recognize human actions, interest regions are segmented from the background using blob extraction method and tracked continuously using generic model. Then a MHI (Motion History Image) for a tracked person is constructed by silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. When an emergency is detected, a mobile robot can help to diagnose the status of the person in the situation. To send the mobile robot to the proper position, we implement mobile robot navigation algorithm based on the distance between the person and a mobile robot. We validate our system by showing emergency detection rate and emergency response demonstration using the mobile robot.

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