Efficient Maximally Stable Extremal Region (MSER) Tracking

Donoser, Michael; Bischof, Horst

doi:10.1109/cvpr.2006.107

Cited by 201 publications

(125 citation statements)

References 9 publications

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“…Such a sim- ple approximation has trouble tracking non-rigid objects in cluttered scenes. In recent studies [6]- [8], researchers confirmed that using visual saliency can substantially improve segmentation and tracking performance. Saliency based detection/ segmentation framework achieves promising results, especially for objects that can't be well approximated by an ellipse or rectangle.…”

Section: Introductionmentioning

confidence: 97%

Non-rigid Object Tracking as Salient Region Segmentation and Association

Zhao

Sun

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYTracking a non-rigid object in a video in the presence of background clutter and partial occlusion is challenging. We propose a non-rigid object-tracking paradigm by repeatedly detecting and associating saliency regions. Saliency region segmentation is operated in each frame. The segmentation results provide rich spatial support for tracking and make the reliable tracking of non-rigid object without drifting possible. The precise object region is obtained simultaneously by associating the saliency region using two independent observers. Our formulation is quite general and other salient-region segmentation algorithms also can be used. Experimental results have shown that such a paradigm can effectively handle tracking problems of objects with rapid movement, rotation and partial occlusion.

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Section: Introductionmentioning

confidence: 97%

Non-rigid Object Tracking as Salient Region Segmentation and Association

Zhao

Sun

et al. 2011

IEICE Trans. Inf. & Syst.

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“…Optimized implementations like the approach proposed in [NS08] allow us to process an image of 640x480 pixels in 50 ms on a 2 GHz CPU, which can be further improved with the aid of parallel processing approaches on the GPU of the mobile device. Tracking these regions and computing additional MSERs only from the previously unseen boundary textures allows for a 5-6 fold speedup [TCGP09,DB06].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Tracking features across several video frames allows not only for fast feature extraction [TCGP09,DB06]. Based on the time span a feature is successfully tracked, its stability with respect to view point changes can be estimated.…”

Section: Query Imagementioning

confidence: 99%

Mobile Visual Location Recognition

Schroth¹,

Huitl

Chen

et al. 2011

IEEE Signal Process. Mag.

107

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“…Recently, advancements have been made in the field of wide-baseline image matching [6], [14], [15]. Many state-of-the-art approaches are grounded on the method described in [6] and aim at providing affine invariance by computing feature descriptors after a set of pre-defined warping transformations have been applied to the images to be matched.…”

Section: Related Workmentioning

confidence: 99%

Air-ground localization and map augmentation using monocular dense reconstruction

Förster

Pizzoli

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features. Air-Ground Localization and Map Augmentation Using Monocular Dense ReconstructionChristian Forster, Matia Pizzoli, Davide ScaramuzzaAbstract-We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of airground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-theart approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.

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Efficient Maximally Stable Extremal Region (MSER) Tracking

Cited by 201 publications

References 9 publications

Non-rigid Object Tracking as Salient Region Segmentation and Association

Non-rigid Object Tracking as Salient Region Segmentation and Association

Mobile Visual Location Recognition

Air-ground localization and map augmentation using monocular dense reconstruction

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