Abstract
In this paper, we describe a Proof-of-Concept implementation of an automated system to drive one aspect of a personalised, object-based TV experience, on sports content, such as football and rugby. Our Proof-of-Concept uses a sequence of analytics processes, which can be performed offline or in real time, to allow a new media object to be automatically positioned on a multi-angle broadcast video sequence without occluding any key action, thus enabling additional graphic or video content to be used to personalise the broadcast for individual viewers. First, an object detection algorithm using a deep neural network model detects the players and ball, and its filtered output defines the region of interest within each frame of the video sequence. To avoid occlusion of key action by the media object, the remaining space within the sequence of frames is analysed to propose suitable locations. Our algorithm applies layout rules to ensure the object is placed in the best position based on broadcaster-defined templates (e.g. top-left, top-right, bottom-right and bottom-left). The output shows that our Proof-of-Concepts capable of processing video sequences with multiple camera angles and proposing the start time and duration of the media object without occluding any key action.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Armstrong, M.: Object-based media: A toolkit for building responsive content. In Proceedings of the 32nd International BCS Human Computer Interaction (HCI) Conference, Belfast, UK (2018)
Howells, E., Jackson, D.: Object-based media report. Ofcom, London (2021)
Netflix, "Black Mirror: Bandersnatch," Netflix, 2018. [Online]. Available: https://www.netflix.com/gb/title/80988062. Accessed 11 June 2022
Walker, J., Williams, D., Kegel, I., Gower, A., Jansen, J., Lomas, M., Fjellsten, S.: 2-IMMERSE: a platform for production, delivery, and orchestration of distributed media applications. SMPTE Motion Imaging J 128(7), 45–51 (2019)
Cox, J., Brooks, M., Forrester, I., Armstrong, M.: Moving object-based media production from one-off examples to scalable workflows. SMPTE Motion Imaging J 127(4), 32–37 (2018)
Röggla, T., Li, J., Jansen, J., Fjellsten, S., Kegel, I., Pilgrim, L., Trimby, M., Williams, D., Cesar, P.: From the lab to the OB truck: Object-based broadcasting at the FA Cup in Wembley Stadium. In CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK (2019)
Ibrahim, M., Lohmar, T., El-Essaili, A., d'Allonnes, A.:TV graphics personalization using in-band events. In Proceedings of the In-Programme Personalization for Broadcast (IPP4B) Workshop, ACM TVX2017, Hilversum, The Netherlands (2017)
ITU-R: Artificial intelligence systems for programme production and exchange. BT Series (2019)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA (2009)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, USA (2005)
Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Carranza-García, M., Torres-Mateo, J., Lara-Benítez, P., García-Gutiérrez, J.: On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data. Remote Sensing 13(1), 89 (2021)
Corcoll Andreu, O.: Semantic image cropping. Queen Mary University of London (2018)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: Single shot multibox detector. In: Computer Vision – ECCV 2016: 14th European Conference, Part 1, Amsterdam, The Netherlands (2016)
Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA (2017)
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., Qu, R.: A survey of deep learning-based object detection. IEEE Access 7, 128837–128868 (2019)
Tan, L., Huangfu, T., Wu, L., Chen, W.: Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Med. Inform. Decis. Mak. 21, 324 (2021)
Morera, Á., Sánchez, Á., Moreno, A.B., Sappa, Á.D., Vélez, J.F.: SSD vs. YOLO for detection of outdoor urban advertising panels under multiple variabilities. Sensors 20(16), 4587 (2020)
Alkentar, S., Alsahwa, B., Assalem, A., Karakolla, D.: Practical comparation of the accuracy and speed of YOLO, SSD and Faster RCNN for drone detection. J. Eng. 27(8), 19–31 (2021)
NVIDIA, "NVIDIA DeepStream SDK Developer Guide," 08 March 2020. [Online]. Available: https://docs.nvidia.com/metropolis/deepstream/5.0/dev-guide/index.htmlpage/DeepStream_Development_Guide/deepstream_performance.htmlwwpID0E0YD0HA. Accessed 11 June 2022
Zhong, Y., Wang, J., Peng, J., Zhang, L.: Anchor box optimization for object detection. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, USA (2020)
Stats Perform, "World Leaders in Sport Data," 2022. [Online]. Available: https://www.statsperform.com/opta/. Accessed 11 June 2022
Sani, Y., Mauthe, A., Edwards, C.: Adaptive bitrate selection: a survey. IEEE Commun. Surv. Tutorials 19(4), 2985–3014 (2017)
Fautier, T.: How OTT services can match the quality of broadcast. SMPTE Motion Imaging J. 129(3), 16–25 (2020)
Jackson,: Broadcast lag in live online TV sport streaming frustrates fans. ISP Review, 10 June 2021. [Online]. Available: https://www.ispreview.co.uk/index.php/2021/06/broadcast-lag-in-live-online-tv-sport-streaming-frustrates-fans.html. Accessed 11 June 2022
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy (2017)
NVIDIA, "DeepStream SDK," 2022. [Online]. Available: https://developer.nvidia.com/deepstream-sdk. Accessed 11 June 2022
NVIDIA, "DetectNet_v2 TAO Toolkit 3.0 documentation," 25 August 2021. [Online]. Available: https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html. Accessed 12 November 2021
BT Sport, "Man City vs Liverpool (1-1, 5-4 on pens) | 2019 Community Shield highlights," BT Sport, 4 August 2019. [Online]. Available: https://www.youtube.com/watch?v=k9_tz9bi3rs. Accessed 11 June 2022
Apache Software Foundation: Kafka 3.0 Documentation. 2017. [Online]. Available: https://kafka.apache.org/documentation/. Accessed 11 June 2022
Armstrong, M., Brown, A., Crabb, M., Hughes, C.J., Jones, R., Sandford, J.: Understanding the diverse needs of subtitle users in a rapidly evolving media landscape. SMPTE Motion Imaging J. 125(9), 33–41 (2016)
Law, E.L.-C., van Schaik, P.: Modelling user experience - an agenda for research and practice. Interact. Comput. 22(5), 313–322 (2010)
Law, E. L.-C., Roto, V., Hassenzahl, M., Vermeeren, A.P.O.S., Kort, J.: Understanding, scoping and defining user experience: A survey approach. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'09), Boston, USA (2009)
Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. 22(140), 5–55 (1932)
Kruger, J.-L., Doherty, S., Fox, W., de Lissa, P.: Multimodal measurement of cognitive load during subtitle processing: Same-language subtitles for foreign-language viewers. In: Lacruz, I., Jääskeläinen, R. (eds.) Innovation and expansion in translation process research, pp. 267–294. John Benjamins Publishing Company, Amsterdam, Netherlands (2018)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Allan, B., Kegel, I., Kalidass, S.H. et al. Towards automatic placement of media objects in a personalised TV experience. Multimedia Systems 28, 2175–2192 (2022). https://doi.org/10.1007/s00530-022-00974-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-022-00974-y