The Importance of Bounding Box in Motion Detection

Fauzi, Ahmad; Madenda, Sarifuddin; Ernastuti,; Wibowo, Eri Prasetyo; Masruriyah, Anis Fitri Nur

doi:10.1109/icic50835.2020.9288604

Cited by 1 publication

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After getting the foreground mask by applying background subtraction algorithm, it will use the foreground mask as the area of interest for ROI. ROI area is marked by a bounding box, which is a box with x and y coordinates representing the width and length of the detected object [27]. Building the bounding box requires a background image, foreground image, and foreground mask.…”

Section: Region Of Interestmentioning

confidence: 99%

A Novel Approach to Video Compression using Region of Interest (ROI) Method on Video Surveillance Systems

Hapsari,

Madenda,

Subali

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

With the increasing of criminal actions, people use various surveillance techniques to create a sense of security. One of the most widely used surveillance technique is installing CCTV cameras at various locations. On the surveillance systems, there are other supporting devices apart from CCTV cameras. One of such supporting devices is a hard disk to save the recorded data. The recording on CCTV has two modes: motion detection mode and continuous mode. The continuous mode will record continuously, which affects the amount of hard disk space used. Motion detection mode records one event only, not all recordings, saving hard disk space, however, it may miss some events. Based on these two modes, compression technology is required. The current compression technology applies the ROI method. A ROI (Region of Interest) is the part of the image that wants to filter to form some operations against it. ROI allows coding differently in certain areas of the digital image to have a higher quality than the surrounding area (background). This paper offers a novel approach to saving the foreground frame generated from the ROI method and compressing it. The novel approach will be applied to the AVI, MJPEG 2000, and MPEG-4 video formats. The decompression process is used to restore the original video data to measure the method's performance. To measure the proposed method's performance, it will compare the compression ratio and Peak Signal-to-Noise Ratio (PSNR) with the traditional method without implementing the ROI-based method. The PSNR value in this paper, that measures the quality of the compression result,s are above 40 dB. It indicates that the resulting video is similar to the original video. The ROI-based compression method can increase the compression ratio 5-7 times higher than the existing method for lossy AVI format video. While on MJPEG-2000 and MPEG-4 format video, it increases the compression ratio 7-15 times and 1-3 times, respectively. The PSNR value for the proposed method is above 40 dB, which indicates that the reconstructed video is similar to the original video, even though the pixel values have changed slightly.

show abstract

Section: Region Of Interestmentioning

confidence: 99%