Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision

Antón-Canalís, Luis; Hernández-Tejera, Mario; Sánchez-Nielsen, Elena

doi:10.1109/isda.2006.253905

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…A PSO algorithm was proposed in [12] to drive particles flying over image pixels directly, where object tracking emerged from interaction between particles and their environment. However, this paper contests the hardware implementation of structural similarity measure as fitness function of PSO for Object tracking demonstrating better performance than its counterpart software introduced in [13].…”

Section: Related Workmentioning

confidence: 99%

Efficient hardware implementation of PSO-based object tracking system

Morsi

Abdelhalim

Shehata

2013

2013 International Conference on Electronics, Computer and Computation (ICECCO)

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This paper proposes hardware design, modelling and implementation for object tracking system targeting video sequence based on Particle Swarm Optimization (PSO). The system uses Structural SIMilarity index (SSIM) as PSO's fitness function. In this paper, we describe Particle Swarm Optimisation algorithm and Structural SIMilarity index, and then we show how it has been simplified for hardware implementation and achieved better results than its software implementation counterpart. Finally, we present the proposed algorithm's performance that has been tested and evaluated over different video sequences, showing its adaption for tracking in real-time.

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Section: Related Workmentioning

confidence: 99%

Efficient hardware implementation of PSO-based object tracking system

Morsi

Abdelhalim

Shehata

2013

2013 International Conference on Electronics, Computer and Computation (ICECCO)

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“…Brain computational modelling with multiple agents is not a new idea (Minsky 1988;Chialvo and Millonas 1995). Although the first realisations for computer vision related problems are also not new (Poli and Valli 1993;Liu et al 1997), only more recently it has received considerable attention (Ramos and Almeida 2000;Owechko and Medasani 2005;Antón-Canalís et al 2006;Mobahi et al 2006;Broggi and Cattani 2006;Mazouzi et al 2007;Zhang et al 2008). In general, these models exploit the metaphor of swarming behaviour on social insects.…”

Section: Agent Abstraction Of Covert Attentionmentioning

confidence: 99%

Swarm cognition on off-road autonomous robots

Santana

Correia

2011

Swarm Intell

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This paper contributes with the first validation of swarm cognition as a useful framework for the design of autonomous robots controllers. The proposed model is built upon the authors' previous work validated on a simulated robot performing local navigation on a 2-D deterministic world. Based on the ant foraging metaphor and motivated by the multiple covert attention hypothesis, the model consists of a set of simple virtual agents inhabiting the robot's visual input, searching in a collectively coordinated way for obstacles. Parsimonious and accurate visual attention, operating on a by-need basis, is attained by making the activity of these agents modulated by the robot's action selection process. A by-product of the system is the maintenance of active, parallel and sparse spatial working memories. In short, the model exhibits the self-organisation of a relevant set of features composing a cognitive system. To show its robustness, the model is extended in this paper to handle the challenges of physical off-road robots equipped with noisy stereoscopic vision sensors. Furthermore, an extensive aggregate of biological arguments sustaining the model is provided. Experimental results show the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot's visual input being analysed. Hence, with this system the computational cost of perception is considerably reduced, thus fostering robot miniaturisation and energetic efficiency. This confirms the advantages of using a swarm-based system, operating in an intricate way with action selection, to judiciously Swarm Intell (2011) 5: 45-72 control visual attention and maintain sparse spatial memories, constituting a basic form of swarm cognition.

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“…Trabalhos relevantes, que empregam a técnica de otimização por enxame de partículas, para a realização do rastreamento visual e/ou processamento de imagens são encontrados em [20], [21], [22] e [23]. Em [21] o algoritmo PSÓ e utilizado para detecção de pessoas em imagens de infravermelho, em que cada partícula foi tratada como um detector com escala específica.…”

Section: Otimização Por Enxame De Partículasunclassified

“…: um Filtro de Kalman) para realizar o rastreamento do objeto. Em [22] foi criada uma estrutura baseada no algoritmo PSO, a qual foi empregada diretamente nos pixels da imagem e onde o objeto rastreado era detectado pela interação entre as partículas. Na Subseção 3.1 são apresentados a idéia básica de desenvolvimento da técnica, proposta por [18] com maiores detalhes.…”

Section: Otimização Por Enxame De Partículasunclassified

Rastreamento Visual de Objetos Utilizando Otimização por Enxame de Partículas

Vidal¹

2011

L&NLM

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Resumo-Neste trabalhoé proposto um algoritmo de rastreamento visual de objetos, no qual emprega metodologias de otimização por enxame de partículas(PSO). O principal objetivo foi o desenvolvimento de uma estrutura para algoritmos de rastreamento em imagem, na qual a conseqüência natural, e necessária, foi o desenvolvimento de melhorias tanto na robustez, quanto nos resultados de trajetórias dos objetos de interesse em se rastrear. Com o intuito de aprimorar as técnicas de rastreamento já desenvolvidas, e ainda utilizar de recursos com o aumento da capacidade de processamento atuais, optou-se pelo o acoplamento do algoritmo com filtragem estocásticaàs técnicas de PSO. Nas quais tornaram o algoritmo modificado com maior robustez e com melhorias na descrição de trajetórias dos objetos rastreados. Palavras-chave-Rastreamento de objetos em imagens, otimização por enxame de partículas, window-matching.

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Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision

Cited by 17 publications

References 17 publications

Efficient hardware implementation of PSO-based object tracking system

Efficient hardware implementation of PSO-based object tracking system

Swarm cognition on off-road autonomous robots

Rastreamento Visual de Objetos Utilizando Otimização por Enxame de Partículas

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