{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:08:08Z","timestamp":1787936888352,"version":"build-2784847793"},"reference-count":28,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018JM5029"],"award-info":[{"award-number":["2018JM5029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A low-cost and power-efficient video surveillance system, named XDMOM, is developed for real-time moving object detection outdoors or in the wild. The novel system comprises four parts: imaging subsystem, video processing unit, power supply, and alarm device. The imaging subsystem, which consists of a dual-spectrum camera and rotary platform, can realize 360-degree and all-day monitoring. The video processing unit uses a power-efficient NVIDIA GeForce GT1030 chip as the processor, which ensures the power consumption of the whole system maintains a low level of 60~70 W during work. A portable lithium battery is employed to supply power so that the novel system can be used anywhere. The work principle is also studied in detail. Once videos are recorded, the single-stage neural network YOLOv4-tiny is employed to detect objects in a single frame, and an adaptive weighted moving pipeline filter is developed to remove pseudo-targets in the time domain, thereby reducing false alarms. Experimental results show that the overall correct alarm rate of the novel system could reach 85.17% in the daytime and 81.79% at night when humans are monitored in real outdoor environments. The good performance of the novel system is demonstrated by comparison with state-of-the-art video surveillance systems.<\/jats:p>","DOI":"10.3390\/s22103905","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:18:08Z","timestamp":1653124688000},"page":"3905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["XDMOM: A Real-Time Moving Object Detection System Based on a Dual-Spectrum Camera"],"prefix":"10.3390","volume":"22","author":[{"given":"Baoquan","family":"Shi","sequence":"first","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi\u2019an 710071, China"},{"name":"Center of Innovative Industrial Design, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weichen","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi\u2019an 710071, China"},{"name":"Center of Innovative Industrial Design, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechano-Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Shaanxi Key Laboratory of Space Extreme Detection, Xidian University, Xi\u2019an 710071, China"},{"name":"Center of Innovative Industrial Design, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100310","DOI":"10.1016\/j.cosrev.2020.100310","article-title":"Moving objects detection with a moving camera: A comprehensive review","volume":"38","author":"Chapel","year":"2020","journal-title":"Comput. 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