Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System

Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 22; p. 8791
Main Authors: Hsia, Shih-Chang, Wang, Szu-Hong, Wei, Chung-Mao, Chang, Chuan-Yu
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 14.11.2022
MDPI
Subjects:
Algorithms
camera sensing
Cameras
Cost control
Deep learning
Facial recognition technology
Humans
motion detection
Movement
Neural networks
object tracking
Signal processing
Surveillance
surveillance system
object tracking
camera sensing
motion detection
surveillance system
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360° horizontal and 90° vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22228791