Target tracking in nonuniform illumination conditions using locally adaptive correlation filters

Díaz-Ramírez, Víctor H.; Picos, Kenia; Kober, Vitaly

doi:10.1016/j.optcom.2014.02.063

Cited by 51 publications

(12 citation statements)

References 40 publications

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“…10 It can be seen, that the proposed algorithm is adapted online in each frame without the need of any o-line supervised training process. 3 The operation of the algorithm is depicted in Fig. 1, and their steps are summarized in Algorithm 1.…”

Section: Target Tracking With Local Adaptive Correlation Filtering Anmentioning

confidence: 99%

“…12, 13 Some algorithms require a supervised training process before the tracking algorithm starts to operate. 3 Other algorithms only require a minimum knowledge about the target since they rely on online learning and adaptation mechanisms. 7,12 Recently, a tracking algorithm that combines and interest points detector and a kernel-based tracker has been suggested.…”

Section: Introductionmentioning

confidence: 99%

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Target tracking using interest point detection and correlation filtering

2014

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A reliable method for real-time target tracking is presented. The method is based on an interest point detector and a bank of locally adaptive correlation lters. The point detector is used to identify local regions in the observed scene around potential location of the target. The bank of correlation lters is employed to reliably detect the target and accurately estimate its position within the scene, by processing the local regions identied by the detector. Using information of past state estimates of the target the proposed algorithm predicts the state of the target in the next frame in order to perform a fast and accurate target tracking by focussing signal processing only on small regions of the scene in each frame. In order to achieve a real-time operation performance the proposed algorithm is implemented in a graphics processing unit. Experimental results obtained with the proposed method are presented, discussed, and compared with those obtained with a similar state-of-the-art target tracking algorithm.

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Section: Target Tracking With Local Adaptive Correlation Filtering Anmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target tracking using interest point detection and correlation filtering

2014

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“…Nowadays, object recognition based on correlation filters receives much research interest due to its high impact in reallife activities, such as video surveillance, human-computer interaction, robotics, biometrics, and target tracking [6][7][8][9][10][11][12]. Correlation filtering is a powerful technique for object recognition because of its ability to perform two essential tasks simultaneously: detection of a target within an observed scene and computation of the exact position of the detected object [13,14].…”

Section: Introductionmentioning

confidence: 99%

Reliable Recognition of Partially Occluded Objects with Correlation Filters

Ruchay

Kober

González-Fraga

2018

Mathematical Problems in Engineering

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Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

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“…Recognition methods involving template matching are not useful in some cases, for instance, when articulation changes global features like the object outline. So, conventional correlation filters without training may yield a poor performance to recognize objects possessing incomplete information [21,22,23]. Adaptive approach to the filter design helps us to synthesize adaptive filters for object tracking [24,25].…”

Section: Introductionmentioning

confidence: 99%

Real-time tracking of multiple objects with locally adaptive correlation filters

Ruchay¹,

Kober²,

Chernoskulov³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects with invariance to pose, occlusion, clutter, and illumination variations. The algorithm employs a prediction scheme and composite correlation filters. The filters are synthesized with the help of an iterative algorithm, which optimizes discrimination capability for each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.

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Target tracking in nonuniform illumination conditions using locally adaptive correlation filters

Cited by 51 publications

References 40 publications

Target tracking using interest point detection and correlation filtering

Target tracking using interest point detection and correlation filtering

Reliable Recognition of Partially Occluded Objects with Correlation Filters

Real-time tracking of multiple objects with locally adaptive correlation filters

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