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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 22; číslo 22; s. 8791
Hlavní autoři:	Hsia, Shih-Chang, Wang, Szu-Hong, Wei, Chung-Mao, Chang, Chuan-Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 14.11.2022 MDPI
Témata:	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
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Abstract	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.
AbstractList	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.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. 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.
Audience	Academic
Author	Chang, Chuan-Yu Wei, Chung-Mao Hsia, Shih-Chang Wang, Szu-Hong
AuthorAffiliation	2 Department of Information Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan 1 Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan
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Cites_doi	10.1109/TITS.2019.2917560 10.1016/j.patcog.2017.09.040 10.1109/ACCESS.2020.3024926 10.1109/ACCESS.2019.2955387 10.1109/TCSVT.2019.2897980 10.1109/TCSVT.2016.2589879 10.1109/TITS.2018.2888698 10.1109/ACCESS.2021.3074319 10.1109/ACCESS.2018.2803787 10.1109/LRA.2021.3057003 10.1007/s11554-020-01050-2 10.1109/TCE.2017.014979 10.1109/TIP.2018.2878349 10.1109/JSEN.2021.3091018 10.1109/JSTARS.2019.2957484 10.1049/iet-ipr.2018.6175 10.1109/TIP.2021.3108405 10.1109/TIP.2019.2922095 10.1109/ACCESS.2020.3020818 10.1016/j.patcog.2017.09.009 10.1109/ACCESS.2020.3008262 10.1109/ACCESS.2019.2891943 10.1109/TII.2019.2921652 10.1007/s11554-020-00958-z 10.1109/ACCESS.2019.2931820
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SubjectTerms	Algorithms camera sensing Cameras Cost control Deep learning Facial recognition technology Humans motion detection Movement Neural networks object tracking Signal processing Surveillance surveillance system
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Title	Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System
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