An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)
A. Nirmala1, S. Arivalagan2, R. Arunkumar3
1A. Nirmala, Research Scholar, Annamalai University, Chidambaram, India.
2Dr. S. Arivalagan, Assistant Professor, Annamalai University, Chidambaram, India.
3Dr. R. Arunkumar, Assistant Professor, Annamalai University, Chidambaram, India.
Manuscript received on 29 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 607-613 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7569078919/19©BEIESP | DOI: 10.35940/ijitee.I7569.078919
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely.
Index Terms: Computer Vision, Mask R-CNN, MOT, Recognition.
Scope of the Article: Pattern Recognition