Fast texture-based tracking and delineation using texture entropy

Shahrokni, Ali; Drummond, Tom; Fua, Pascal

doi:10.1109/iccv.2005.97

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…In the literature, many features have been used to represent the target such as color [5], shape [25], texture [34] and kinematic features [6]. For a number of tracking applications, color is an optimal choice because of its descriptive power and the fact that color information is readily accessible in the image.…”

Section: Appearance Modelingmentioning

confidence: 99%

Adaptive NormalHedge for robust visual tracking

et al. 2015

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a b s t r a c tIn this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

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Section: Appearance Modelingmentioning

confidence: 99%

Adaptive NormalHedge for robust visual tracking

et al. 2015

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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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“…Appearance modeling is used to represent the tracked target using information extracted from the target region. In the literature, widely used features are color [6], shape [7], texture [8], combination of color and texture [9], SIFT features [10], combination of color and SIFT [11] and attentional features [12]. For many tracking situations, color is a good choice for representing the tracked target because of its descriptive power and the fact that color information is readily accessible in image.…”

Section: Appearance Modelingmentioning

confidence: 99%