INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Real Time Object Detection Deep Reinforcement Learning Model: RODRLM
Authors Name:
Mrs Sonal Tiwari
Unique Id:
IJSDR2208070
Published In:
Volume 7 Issue 8, August-2022
Abstract:
Visual tracking is a challenging problem since it usually faces adverse factors, such as object deformation, fast motion, occlusion, and background clutter in practical applications. Reinforcement learning based Action-Decision Network (ADNet) has shown great potential for object tracking. However ,ADNet has some shortcomings in optimal action selection and action reward, and suffers from in efficient tracking. To this end, real time object detection deep reinforcement learning model come in tracking method , RODRLM improve efficiency and accuracy in real time object tracking . Reinforcement Learning based real time object detection framework using deep machine learning model is a unique and important technique using which user can get quality output and can use in many system for getting efficient results. In this work we propose object detection with deep reinforcement learning by which we train the agent to extract the features of sequence of the frame and with a trained agent we detect the object present in video. The proposed technique improve the feature extraction ability of its convolution layers. Then, in the reinforcement learning based training phase, both the selection criteria for optimal action and the reward function are redesigned separately to explore more appropriate action and eliminate useless action .Finally, an effective online adaptive update strategy is proposed to adapt to the appearance changes or deformation of the object during actual tracking. Specially, meta-learning is utilized to pursue the most appropriate parameters for the network so that the parameters are closer to the optimal ones in the subsequent tracking process.
"Real Time Object Detection Deep Reinforcement Learning Model: RODRLM", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 8, page no.515 - 521, August-2022, Available :http://www.ijsdr.org/papers/IJSDR2208070.pdf
Downloads:
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Publication Details:
Published Paper ID: IJSDR2208070
Registration ID:201171
Published In: Volume 7 Issue 8, August-2022
DOI (Digital Object Identifier):
Page No: 515 - 521
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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