Abstract
| Original language | English |
|---|---|
| Title of host publication | 2019 International Conference on Information Science and Communication Technology, ICISCT 2019 |
| Publisher | IEEE |
| Pages | 6-11 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728104478 |
| ISBN (Print) | 9781728104485 |
| DOIs | |
| Publication status | Published - 29 Jul 2019 |
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Keywords
- complexity theory
- cameras
- object detection
- motion detection
- image coding
- animals
- visualisation
Cite this
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Low complexity object detection with background subtraction for intelligent remote monitoring. / Nazir, Sajid; Hamdoun, Hassan; Kaleem, Muhammad.
2019 International Conference on Information Science and Communication Technology, ICISCT 2019. IEEE, 2019. p. 6-11.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Low complexity object detection with background subtraction for intelligent remote monitoring
AU - Nazir, Sajid
AU - Hamdoun, Hassan
AU - Kaleem, Muhammad
N1 - Acceptance in SAN Title not found online Query to author re conf. paper or proceeding ET 21/5/19 ^Author confimed conf. paper 22/5/19 AAM: no embargo
PY - 2019/7/29
Y1 - 2019/7/29
N2 - Advancements in digital technologies have enabled cost-effective deployments of visual sensor nodes that can detect a motion event in the coverage area. The real world field remote monitoring image capture conditions, for example, in security and ecological studies are affected by wind, rain, snow, sunlight etc. and are seldom ideal. Motion detection is a precursor to subsequent intelligent processing on the image to extract information. Less complex object detection techniques often rely on maintaining a background image and subtracting foreground image, purporting to have an object in it, to create a difference image to determine the presence of a moving object. Correct object detection is critical as otherwise resulting false positive (without a moving object) images needlessly invoke further processing, storage and analysis. In this paper, we review background subtraction techniques and propose an image differencing technique that can significantly reduce the algorithm complexity along with other associated advantages. The results of proposed reduced image subset are provided to highlight the benefits.
AB - Advancements in digital technologies have enabled cost-effective deployments of visual sensor nodes that can detect a motion event in the coverage area. The real world field remote monitoring image capture conditions, for example, in security and ecological studies are affected by wind, rain, snow, sunlight etc. and are seldom ideal. Motion detection is a precursor to subsequent intelligent processing on the image to extract information. Less complex object detection techniques often rely on maintaining a background image and subtracting foreground image, purporting to have an object in it, to create a difference image to determine the presence of a moving object. Correct object detection is critical as otherwise resulting false positive (without a moving object) images needlessly invoke further processing, storage and analysis. In this paper, we review background subtraction techniques and propose an image differencing technique that can significantly reduce the algorithm complexity along with other associated advantages. The results of proposed reduced image subset are provided to highlight the benefits.
KW - complexity theory
KW - cameras
KW - object detection
KW - motion detection
KW - image coding
KW - animals
KW - visualisation
U2 - 10.1109/CISCT.2019.8777413
DO - 10.1109/CISCT.2019.8777413
M3 - Conference contribution
SN - 9781728104485
SP - 6
EP - 11
BT - 2019 International Conference on Information Science and Communication Technology, ICISCT 2019
PB - IEEE
ER -