ADNet: A Deep Network for Detecting Adverts M. Hossari, S. Dev, M. Nicholson, K. McCabe, A. Nautiyal, C. Conran, J. Tang, W. Xu, and F. Pitié The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
People consume a lot of content adaptcentre.ie We Never Wanted to Talk to the Person Next to Us: http://republicofweb.org/weblog/?p=1032.
People consume a lot of content adaptcentre.ie ● People have an insatiable appetite for media We Never Wanted to Talk to the Person Next to Us: http://republicofweb.org/weblog/?p=1032.
Technology Create Opportunity adaptcentre.ie Millennials in Singapore spend almost 3.4 hours a day on their mobile phones: https://www.straitstimes.com/tech/smartphones/millennials-in-singapore-spend-almost-34-hours-a-day-on-their-mobile-phones-study
Technology Create Opportunity adaptcentre.ie ● 5 billion people now have mobiles phones ● We consume more content on demand ● Using services that can personalise content to the user Millennials in Singapore spend almost 3.4 hours a day on their mobile phones: https://www.straitstimes.com/tech/smartphones/millennials-in-singapore-spend-almost-34-hours-a-day-on-their-mobile-phones-study
Opening the door to personalise marketing adaptcentre.ie Illustration by M. Nicholson, October 2018.
Benefits of personalised marketing adaptcentre.ie Make it easy to seamlessly integrate advertisements into ● on-demand video content Allow user access to uninterrupted content; unlike pre-roll, ● mid-roll, and post-roll advertisements Allow existing advertisement to be automatically replaced by ● personalised ads Applications in other sectors: information display, education, ● social outreach Illustration by M. Nicholson, October 2018.
The Industry Challenge adaptcentre.ie How to accurately identify the video frame that contain an advertisement/billboard? Manual identification of adverts in a video is cumbersome and time-consuming. No similar system exists for accurate detection of advert in a video frame. Diverse scene illumination and severe occlusion make such tasks challenging.
The Solution: AdNet for detecting adverts adaptcentre.ie The recognition module takes images (video frames) as input. It then classifies those images as containing an advert or not (“ad” or “no ad”). We propose using the VGG based network that we call ADNET . Based on VGG19 Architecture. Transfer learning using pre-trained "ImageNet" model and froze all layers apart from last 5 layers. Added 3 fully connected layers with a softmax layer as the output layer.
Dataset adaptcentre.ie We train our ADNet model on a composite dataset, that consists of both positive- and negative- examples of billboard detection. Sources of images are: - Mapillary Vistas dataset 1 - Microsoft COCO dataset 2 [1] Neuhold, G., Ollmann, T., Bulo, S.R., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: ICCV. pp. 5000–5009 (2017) [2] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
Subjective Evaluation adaptcentre.ie
Subjective Evaluation adaptcentre.ie
Objective Evaluation adaptcentre.ie We report the classification accuracy of ADNet. Suppose, TP, FP, TN and FN denote the true positive, false positive, true negative and false negative respectively: Classification Accuracy = (TP+TN)/(TP+TN+FP+FN)
The Developed System adaptcentre.ie Illustration by M. Nicholson, October 2018. More details on the advert creation system: https://arxiv.org/pdf/1808.00163.pdf
Info and Contact Prof. François Pitié, The ADAPT Centre, PITIEF@tcd.ie. The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
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