Implementation of Modified Mask RCNN
Date Archana1, Shah Sanjeevani2
1Archana Date* , Sinhgad College of Engineering, Pune, India. (Savitribai Phule Pune University) Pune.
2Sanjeevani K. Shah, Smt. Kashibai Navale College of Engineering, Pune, India. (Savitribai Phule Pune University) Pune.
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 4167-4172 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6541129219/2019©BEIESP | DOI: 10.35940/ijitee.B6541.129219
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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: Detecting camouflage moving object from the video sequence is the big challenge in computer vision. To detect moving object from dynamic background is also very difficult as the background is also detected as moving object. Mask RCNN is a deep neural network which solves the problem of separation of instances of same object in machine learning or computer vision. Thus, it separates different objects in video. It is the extension of faster RCNN in which an extra branch is added to create an object mask simultaneously along with bounding box and classifier. After giving input, Mask RCNN gives the rectangle around the object, class to which object belong and object mask. This article introduces Mask RCNN algorithm along with some modifications for target detection from dynamic background and also for camouflage handling. After target object detection, contrast limited adaptive histogram equalization is applied. Morphological operations are used to improve results. For both challenges quantitative and qualitative measures were obtained and compared with the existing algorithms. Our method efficiently detects the moving object from input sequence and gives best results in both situations.
Keywords: Camouflage, Object, Dynamic, Moving, Video Surveillance.
Scope of the Article: Foundations Dynamics