Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

Benarab, D.; Napoléon, Thibault; Alfalou, Ayman; Verney, Antoine; Hellard, Philippe

doi:10.1016/j.optcom.2015.07.056

Cited by 16 publications

(15 citation statements)

References 29 publications

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“…It counted the number of pixels, which had similar colours in a fixed range (Zhang et al, ). For a standard colour image, the CH was generated by colour discretization into 64 bins (Benarab et al, ), and then pixel numbers were summed in each bin (Hsu & Chou, ). Figure showed the details of calculating CH.…”

Section: Methodsmentioning

confidence: 99%

Fruit classification by biogeography‐based optimization and feedforward neural network

et al. 2016

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Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit-classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four-step pre-processing was employed. Second, the features (colour, shape, and texture) were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit-classification system based on biogeography-based optimization (BBO) and feedforward neural network (FNN) was proposed, with the short name of BBO-FNN. The experiment employed over 1653 chromatic fruit images (18 categories) by fivefold stratified cross-validation. The results showed that the proposed BBO-FNN yielded an overall accuracy of 89.11%, which was higher than the five state-of-the-art methods: genetic algorithm-FNN, artificial bee colony-FNN, particle swarm optimization-FNN, kernel support vector machine, and ant colony optimization-FNN. Also, the BBO-FNN achieved the same accuracy as fitness-scaling chaotic artificial bee colony-FNN, but it performed much faster than the latter. The proposed BBO-FNN was effective in fruit-classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.

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Section: Methodsmentioning

confidence: 99%

Fruit classification by biogeography‐based optimization and feedforward neural network

et al. 2016

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“…We proposed in our previous work [1,8,9] an optimized swimmers tracking system based mainly on the head as the body part to be tracked. is system requires, as an input, a region of interest around the swimmer to be tracked and a reference image of his head.…”

Section: Tracking Initialization Using the Scaled Composite Jtc Approachmentioning

confidence: 99%

“…Our objective is to accurately detect the swimmer's head and initialize the swimmer tracking system developed in the previous work [1,8,9]. For this, we apply the proposed Scaled Composites JTC approach to detect the swimmer in the first images of the video sequence.…”

Section: Pce-based Decisionmentioning

confidence: 99%

“…e second module consists of an automatic initialization of the tracking process with a reference image of the swimmer's head. In our previous work [1,8,9], this initial reference is selected manually to launch the tracking process. To detect it automatically, we propose the Scaled Composite JTC approach which is based on the NL-JTC correlation technique [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches

Benarab

Napoléon

Alfalou

et al. 2020

International Journal of Optics

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In order to accompany the swimming coaches in evaluating high-level swimmers, we developed a prototype for instantaneous speed estimation. To achieve this, we proposed and validated, in a previous work, a swimmer tracking system based on data fusion. However, the initialization phase is done manually, and our aim, in this paper, is to automate this process. First, we propose a region of interest localization module that allows the detection of the first appearance of the swimmer in the lane as well as the restriction of the region of interest around him. This module is based on the method a contrario which consists of modeling the random noise corresponding to the water and detecting the structured movement relative to the swimmer motion. To do that, we calibrate the pool using DLT (Direct Linear Transform) technique, extract the concerned lane, apply the frame difference approach to detect the moving objects, and then decompose the lane into blocs and classify them into swimmer motion or noise. Second, in order to detect the swimmer’s head, we propose the Scaled Composite JTC which is based on the NL-JTC correlation technique. The input plane of this latter includes a target and a reference image. The first is the region of interest detected by the method a contrario. The second consists of a Scaled Composite Reference. The tests conducted on real video sequences of French swimming championships (Limoges 2015) showed very good results in terms of region of interest localization and swimmer’s head detection which allows a reliable initialization for the tracking system.

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“…When the frames from the reference and the input sequence do match, a correlation peak is generated in the output plane. In another application, Benarab and coworkers developed an optimized swimmer tracking system based on the dynamical fusion of correlation and color histogram techniques [16].…”

Section: A Brief Review On Correlation-based Face Recognitionmentioning

confidence: 99%

New perspectives in face correlation: discrimination enhancement in face recognition based on iterative algorithm

Wang

Alfalou

Brosseau

2016

Optical Pattern Recognition XXVII

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Here, we report a brief review on the recent developments of correlation algorithms. Several implementation schemes and specific applications proposed in recent years are also given to illustrate powerful applications of these methods. Following a discussion and comparison of the implementation of these schemes, we believe that all-numerical implementation is the most practical choice for application of the correlation method because the advantages of optical processing cannot compensate the technical and/or financial cost needed for an optical implementation platform. We also present a simple iterative algorithm to optimize the training images of composite correlation filters. By making use of three or four iterations, the peak-to-correlation energy (PCE) value of correlation plane can be significantly enhanced. A simulation test using the Pointing Head Pose Image Database (PHPID)illustrates the effectiveness of this statement. Our method can be applied in many composite filters based on linear composition of training images as an optimization means.

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Optimized swimmer tracking system by a dynamic fusion of correlation and color histogram techniques

Cited by 16 publications

References 29 publications

Fruit classification by biogeography‐based optimization and feedforward neural network

Fruit classification by biogeography‐based optimization and feedforward neural network

Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches

New perspectives in face correlation: discrimination enhancement in face recognition based on iterative algorithm

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