Supereye: smart advertisement insertion for online video streaming

Bulkan, Utku; Dagiuklas, Tasos; Iqbal, Muddesar

doi:10.1007/s11042-022-13469-9

Multimed Tools Appl

2022

DOI: 10.1007/s11042-022-13469-9

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Supereye: smart advertisement insertion for online video streaming

Utku Bulkan

Tasos Dagiuklas

Muddesar Iqbal

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2024

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Cited by 2 publications

References 31 publications

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Out of the fog: fog computing-enabled AI to support smart marketing management

Hornik,

Ofir,

Rachamim

2024

Manag Rev Q

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Marketing and consumer research use a variety of data and electronic measurement devices for research, theory-building, and applied decision-making. Managing data deluge produced by ‘smart devices’ and internet of things (IoT) actuators and sensors is one of the challenges faced by managers when using IoT systems. With the advent of the cloud-based IoT and artificial intelligence, which are advancing a ‘smart world’ and introducing automation in many application areas, such as ‘smart marketing,’ a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like, ChatGPT and Bart, and technologies like nanotechnology. The article introduces the marketing community to a recent computing development: IoT-driven fog computing (FC)—an emerging concept that decentralizes operations, management, and data into the network utilizing a distributed and federated computing paradigm. Although numerous research studies have been published on ‘smart’ applications, none hitherto have been conducted on fog-based smart marketing. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous marketing applications requiring real-time processing of ‘big data’ typical of smart marketing ecosystems.

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Out of the fog: fog computing-enabled AI to support smart marketing management

Hornik,

Ofir,

Rachamim

2024

Manag Rev Q

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AdSegNet: a deep network to localize billboard in outdoor scenes

Dhang,

Zhang,

Dev

2024

SIViP

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Over the past decade, there has been a remarkable expansion in the popularity of online videos, due to the variety of content that has become accessible on the internet. This expansion presents an opportunity for advertising and marketing agencies to take advantage of targeted advertisements. Targeted advertisements can be accomplished by replacing an existing advertisement within an image frame with a new one. There is a limited amount of research on the general task of localizing billboard or advertisement board in outdoor scenes. Therefore, in this study, we proposed a deep neural network that uses a fusion of VGG16 and SegNet architecture to accurately identify the location of an advertisement in an image frame. To evaluate the effectiveness of our proposed method, we compare our proposed method to other semantic segmentation algorithms using a publicly available dataset of outdoor scenes annotated with binary maps of billboards. Our experimental results show that the proposed method achieves 98.58% training accuracy for billboard localization, while testing results gave 96.43% testing accuracy. Additionally, the low RMSE score of our AdSegNet model suggests that it can accurately determine the four corners of the billboard. Therefore, our approach could be beneficial to advertising and marketing agencies that seek to utilize targeted advertisements

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Supereye: smart advertisement insertion for online video streaming

Cited by 2 publications

References 31 publications

Out of the fog: fog computing-enabled AI to support smart marketing management

Out of the fog: fog computing-enabled AI to support smart marketing management

AdSegNet: a deep network to localize billboard in outdoor scenes

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