Multi-modal tracking using texture changes

Kemp, Christopher; Drummond, Tom

doi:10.1016/j.imavis.2006.12.014

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…However, it still maintain a unique hypothesis on the camera pose. Also motivated by the robustness required for UAV application, [14] proposed to use RANSAC to maintain a multimodal representation of the posterior density in a particle-filter inspired way. Our tracking approach differs from [14] on two main points.…”

Section: B Related Workmentioning

confidence: 99%

“…2) Second, while in [14] all the pose hypotheses are derived from the image, in our approach we propose to combine hypotheses generated at the pose level, as propagated through a classic condensation scheme [13], with the multiple hypotheses generated thanks to our k-mean based registration process (see Section III). The next two sections describe our vision-based approach in details.…”

Section: B Related Workmentioning

confidence: 99%

“…3). In [14] this was achieved via a RANSAC approach. Here, we express this as a typical classification problem, and propose to tackle it using a k-mean clustering algorithm [10].…”

Section: A Determining the Underlying Edgesmentioning

confidence: 99%

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3-D Model-Based Tracking for UAV Indoor Localization

Teulière

Marchand

Eck

2015

IEEE Trans. Cybern.

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This paper proposes a novel model-based tracking approach for 3-D localization. One main difficulty of standard model-based approach lies in the presence of low-level ambiguities between different edges. In this paper, given a 3-D model of the edges of the environment, we derive a multiple hypotheses tracker which retrieves the potential poses of the camera from the observations in the image. We also show how these candidate poses can be integrated into a particle filtering framework to guide the particle set toward the peaks of the distribution. Motivated by the UAV indoor localization problem where GPS signal is not available, we validate the algorithm on real image sequences from UAV flights.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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3-D Model-Based Tracking for UAV Indoor Localization

Teulière

Marchand

Eck

2015

IEEE Trans. Cybern.

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“…Common features used in visual tracking include color, 9,15 active contour, 11,12 texture, 27,28 and so on. In general, the most desirable property of a visual feature is its uniqueness so that the objects can be easily distinguished in the feature space.…”

Section: "Three Factors" In Visual Trackingmentioning

confidence: 99%

Visual tracking method based on cuckoo search algorithm

et al. 2015

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Cuckoo search (CS) is a new meta-heuristic optimization algorithm that is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It has been found to be efficient in solving global optimization problems. An application of CS is presented to solve the visual tracking problem. The relationship between optimization and visual tracking is comparatively studied and the parameters' sensitivity and adjustment of CS in the tracking system are experimentally studied. To demonstrate the tracking ability of a CS-based tracker, a comparative study of tracking accuracy and speed of the CS-based tracker with six "state-of-art" trackers, namely, particle filter, meanshift, PSO, ensemble tracker, fragments tracker, and compressive tracker are presented. Comparative results show that the CS-based tracker outperforms the other trackers.

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Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Motion Analysis and Classification

Yang

Leykin

Hammoud

Augmented Vision Perception in Infrared

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Multi-modal tracking using texture changes

Cited by 8 publications

References 17 publications

3-D Model-Based Tracking for UAV Indoor Localization

3-D Model-Based Tracking for UAV Indoor Localization

Visual tracking method based on cuckoo search algorithm

Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Motion Analysis and Classification

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