Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution

Authors

  • Kevin Kevin Universitas Prima Indonesia
  • Nico Gunawan Universitas Prima Indonesia
  • Mariana Erfan Kristiani Zagoto Universitas Prima Indonesia
  • Laurentius Laurentius Universitas Prima Indonesia
  • Amir Mahmud Husein Universitas Prima Indonesia

DOI:

10.33395/sinkron.v4i1.10248

Keywords:

Camera, Video, Human, Object, & Convolution Neural Network

Abstract

Abstract The purpose of this study is to compare the video quality between the Samsung HP camera and the Xiaomi HP camera. The object of study was UNPRI students who walked through the front yard of the UNPRI SEKIP campus. Here we test how accurate the camera's HP capture capacity is used to take the video. The method used to test this research is the Convolution Neural Network method. Object detection and recognition aim to detect and classify objects that can be applied to various fields such as face, human, pedestrian, vehicle detection (Pedoeem & Huang, 2018), besides the ability to find, identify, track and stabilize objects in various poses and important backgrounds in many real-time video applications. Object detection, tracking, alignment and stabilization have become very interesting fields of research in the vision and recognition of computer patterns due to the challenging nature of several slightly different objects such as object detection, where the algorithm must be precise enough to identify, track and center an object from the others

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References

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., . . . Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Huang, R., Pedoeem, J., & Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. IEEE International Conference on Big Data (Big Data) (pp. 2503–2510). Seattle, WA, USA, USA: IEEE. doi:https://doi.org/10.1109/BigData.2018.8621865

Li, Y., Li, J., Lin, W., & Li, J. (2018). Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages. 29TH BRITISH MACHINE VISION CONFERENCE (pp. 1-12). Northumbria: Northumbria University.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. European Conference on Computer Vision (pp. 21-37). Amsterdam, The Netherlands: Springer.

Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Washington, Washington, USA.

Ren, S., He, K., & Girshick, R. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (pp. 1137–1149). IEEE. doi:https://doi.org/10.1109/TPAMI.2016.257703

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How to Cite

Kevin, K., Gunawan, N., Zagoto, M. E. K., Laurentius, L., & Husein, A. M. (2019). Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 4(1), 260-262. https://doi.org/10.33395/sinkron.v4i1.10248

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