Region-of-Interest Extraction Method to Increase Object-Detection Performance in Remote Monitoring System
Source abstract
Abstract
This study proposes an image data preprocessing method to improve the efficiency of transmitting and processing images for object detection in distributed IoT systems such as digital CCTV. The proposed method prepares a background image using Gaussian Mixture-based modeling with a series of continuous images in the video. Then it is used as the reference to be compared with a target image to extract the ROIs by our DSSIM-based area filtering algorithm. The background areas beside the ROIs in the image are filled with a single color—either black or white to reduce data size, or a highly saturated color to improve object detection performance. Our implementation results confirm that the proposed method can considerably reduce the network overhead and the processing time at the server side. From additional experiments, we found that the model’s inference time and accuracy for object detection can be significantly improved when our two new ideas are applied: expanding ROI areas to improve the objectness of each object in the image and filling the background with a highly saturated color.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrared Target Detection Methodologies · Industrial Vision Systems and Defect Detection · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Smart ICT Convergence HRD Center, Pai-Chai University, Daejeon 35345, Republic of Korea
Department of Software Engineering, Pai-Chai University, Daejeon 35345, Republic of Korea
Funding
Korea government (MSIT)
References (28)
Show source-provided references
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