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


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.

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This work was supported in part by the Australian Research Council (ARC) under Grant DP180100670 and Grant DP180100656, in part by the U.S. Army Research Laboratory under Agreement W911NF-10-2-0022, and in part by the Taiwan Ministry of Science and Technology under Grant MOST 106-2218-E-009-027-MY3.

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Correspondence to Mukesh Prasad.

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Cheng, E.J., Prasad, M., Yang, J. et al. A novel online self-learning system with automatic object detection model for multimedia applications. Multimed Tools Appl 80, 16659–16681 (2021). https://doi.org/10.1007/s11042-020-09055-6

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