Multiple camera video surveillance for customer behaviour analysis in a retail store
Traditional methods of determining in-store customer behaviour inside a retail store do not actually represent the customer's actual behaviour. Customer information like gender and knowledge of actual customer behaviour is important in making business decisions (called business intelligence). T...
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oai:animorepository.dlsu.edu.ph:etd_masteral-117942024-04-08T01:13:25Z Multiple camera video surveillance for customer behaviour analysis in a retail store Guevara, Emmanuel C. Traditional methods of determining in-store customer behaviour inside a retail store do not actually represent the customer's actual behaviour. Customer information like gender and knowledge of actual customer behaviour is important in making business decisions (called business intelligence). This research presents a cost-efficient business intelligence solution using video analytics by utilizing IP cameras, a Wi-Fi router, and a Core® 2 Quad based desktop computer. Since in-store security video was not available from an actual boutique store, video samples from a class room were used instead. The proposed solution was able to provide information on people count to within 20% of the ground truth. Male and female detection error rates using body silhouettes were 29.67% and 40.51% respectively. Movement tracking and distribution of people inside the room were achieved using heat maps by tracking blob centroid locations through time. 2015-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/4956 Master's Theses English Animo Repository |
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Traditional methods of determining in-store customer behaviour inside a retail store do not actually represent the customer's actual behaviour. Customer information like gender and knowledge of actual customer behaviour is important in making business decisions (called business intelligence). This research presents a cost-efficient business intelligence solution using video analytics by utilizing IP cameras, a Wi-Fi router, and a Core® 2 Quad based desktop computer. Since in-store security video was not available from an actual boutique store, video samples from a class room were used instead. The proposed solution was able to provide information on people count to within 20% of the ground truth. Male and female detection error rates using body silhouettes were 29.67% and 40.51% respectively. Movement tracking and distribution of people inside the room were achieved using heat maps by tracking blob centroid locations through time. |
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Guevara, Emmanuel C. |
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Guevara, Emmanuel C. Multiple camera video surveillance for customer behaviour analysis in a retail store |
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Guevara, Emmanuel C. |
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Guevara, Emmanuel C. |
title |
Multiple camera video surveillance for customer behaviour analysis in a retail store |
title_short |
Multiple camera video surveillance for customer behaviour analysis in a retail store |
title_full |
Multiple camera video surveillance for customer behaviour analysis in a retail store |
title_fullStr |
Multiple camera video surveillance for customer behaviour analysis in a retail store |
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Multiple camera video surveillance for customer behaviour analysis in a retail store |
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multiple camera video surveillance for customer behaviour analysis in a retail store |
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2015 |
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https://animorepository.dlsu.edu.ph/etd_masteral/4956 |
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