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A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Centro Tecnológico, Programa de Pós Graduação em Engenharia Elétrica, Universidade Federal do Espírito Santo, Vitória, Espírito Santo 29075-910, Brazil
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Academic Editor: Gwanggil Jeon
Sensors 2021, 21(5), 1903; https://doi.org/10.3390/s21051903
Received: 24 November 2020 / Revised: 29 January 2021 / Accepted: 29 January 2021 / Published: 9 March 2021
(This article belongs to the Section Remote Sensors)
This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate. View Full-Text
Keywords: swarm intelligence; meta-heuristic; dynamic optimization problems; video target tracking; time series forecasts swarm intelligence; meta-heuristic; dynamic optimization problems; video target tracking; time series forecasts
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MDPI and ACS Style

Castro, E.C.d.; Salles, E.O.T.; Ciarelli, P.M. A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking. Sensors 2021, 21, 1903. https://doi.org/10.3390/s21051903

AMA Style

Castro ECd, Salles EOT, Ciarelli PM. A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking. Sensors. 2021; 21(5):1903. https://doi.org/10.3390/s21051903

Chicago/Turabian Style

Castro, Edwards C.d.; Salles, Evandro O.T.; Ciarelli, Patrick M. 2021. "A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking" Sensors 21, no. 5: 1903. https://doi.org/10.3390/s21051903

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