A novel online self-learning system with automatic object detection model for multimedia applications

Eric Juwei Cheng, Mukesh Prasad*, Jie Yang, Ding Rong Zheng, Xian Tao, Domingo Mery, Ku Young Young, Chin Teng Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a novel online self-learning detection system for different types of objects. It allows users to random select detection target, generating an initial detection model by selecting a small piece of image sample and continue training the detection model automatically. The proposed framework is divided into two parts: First, the initial detection model and the online reinforcement learning. The detection model is based on the proportion of users of the Haar-like features to generate feature pool, which is used to train classifiers and get positive-negative (PN) classifier model. Second, as the videos plays, the detecting model detects the new sample by Nearest Neighbor (NN) Classifier to get the PN similarity for new model. Online reinforcement learning is used to continuously update classifier, PN model and new classifier. The experiment shows the result of less detection sample with automatic online reinforcement learning is satisfactory.

Original languageEnglish
JournalMultimedia Tools and Applications
DOIs
StateAccepted/In press - 2020

Keywords

  • Classifier
  • Feature pool
  • Object detection
  • Online learning
  • Real-time learning

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