1 resultado para Movement Patterns
Filtro por publicador
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
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- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Aston University Research Archive (4)
- Avian Conservation and Ecology - Eletronic Cientific Hournal - Écologie et conservation des oiseaux: (1)
- Biblioteca de Teses e Dissertações da USP (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (7)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (90)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (5)
- Biodiversity Heritage Library, United States (5)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (18)
- Brock University, Canada (3)
- CentAUR: Central Archive University of Reading - UK (16)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (4)
- Cochin University of Science & Technology (CUSAT), India (1)
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- Cor-Ciencia - Acuerdo de Bibliotecas Universitarias de Córdoba (ABUC), Argentina (4)
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- Duke University (1)
- Galway Mayo Institute of Technology, Ireland (1)
- Glasgow Theses Service (1)
- Institute of Public Health in Ireland, Ireland (7)
- Instituto Politécnico do Porto, Portugal (20)
- Instituto Superior de Psicologia Aplicada - Lisboa (1)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (46)
- Martin Luther Universitat Halle Wittenberg, Germany (2)
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- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (8)
- Repositório da Produção Científica e Intelectual da Unicamp (15)
- Repositório da Universidade Federal do Espírito Santo (UFES), Brazil (1)
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- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (21)
- SAPIENTIA - Universidade do Algarve - Portugal (3)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Scielo Saúde Pública - SP (109)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (3)
- Universidad de Alicante (1)
- Universidad Politécnica de Madrid (3)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade do Minho (7)
- Universidade dos Açores - Portugal (7)
- Universidade Federal do Pará (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (1)
- Universidade Técnica de Lisboa (2)
- Université de Lausanne, Switzerland (183)
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- Université Laval Mémoires et thèses électroniques (1)
- University of Canberra Research Repository - Australia (2)
- University of Queensland eSpace - Australia (194)
- University of Washington (1)
Resumo:
FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.