HPMug2oMmNrOfxWQHLiEksa6s0hFu9Ox348d7QefarYlaFR5ArkhOwm3Da1pmxmxCtenj1+6luWD#r#n+EPn9L6Ce+9onqnMlT+i ›› 2024, Vol. 40 ›› Issue (4): 89-.DOI: 10.3969/j.issn.1009-0479.2024.04.015
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Abstract: Ａｂｓｔｒａｃｔ：Ｔｈｉｓｓｔｕｄｙａｉｍｓｔｏｅｘｐｌｏｒｅａｎｉｎｔｅｌｌｉｇｅｎｔｂｕｉｌｄｉｎｇｓａｆｅｔｙｍｏｎｉｔｏｒｉｎｇｓｙｓｔｅｍｂａｓｅｄｏｎｅｄｇｅｃｏｍ ｐｕｔｉｎｇｔｏａｄｄｒｅｓｓｔｈｅｃｈａｌｌｅｎｇｅｓｏｆｉｎｔｅｌｌｉｇｅｎｔｂｕｉｌｄｉｎｇｓａｆｅｔｙｍｏｎｉｔｏｒｉｎｇ．Ｆｉｒｓｔｌｙ，ｔｈｅｎｅｅｄｓａｎｄｃｈａｌｌｅｎ ｇｅｓｏｆｉｎｔｅｌｌｉｇｅｎｔｂｕｉｌｄｉｎｇｓａｆｅｔｙｍｏｎｉｔｏｒｉｎｇｗｅｒｅａｎａｌｙｚｅｄ，ｅｓｔａｂｌｉｓｈｉｎｇｔｈｅｐｕｒｐｏｓｅｏｆｔｈｉｓｓｔｕｄｙ．Ｓｅｃｏｎｄ ｌｙ，ａｍｏｎｉｔｏｒｉｎｇｓｙｓｔｅｍａｒｃｈｉｔｅｃｔｕｒｅｂａｓｅｄｏｎｅｄｇｅｃｏｍｐｕｔｉｎｇｗａｓｄｅｓｉｇｎｅｄ，ｕｔｉｌｉｚｉｎｇＹｏｌｏａｎｄＳｌｏｗＦａｓｔ ｍｏｄｅｌｓｔｏｉｍｐｌｅｍｅｎｔｆｕｎｃｔｉｏｎｓｓｕｃｈａｓｆａｃｅｒｅｃｏｇｎｉｔｉｏｎ，ｔａｒｇｅｔｄｅｔｅｃｔｉｏｎ，ｐｏｓｅｒｅｃｏｇｎｉｔｉｏｎ，ａｎｄｂｅｈａｖｉｏｒ ｄｅｔｅｃｔｉｏｎ．Ｓｕｂｓｅｑｕｅｎｔｌｙ，ｔｈｅｓｙｓｔｅｍｓｐｅｒｆｏｒｍａｎｃｅｗａｓｅｖａｌｕａｔｅｄｔｈｒｏｕｇｈｅｘｐｅｒｉｍｅｎｔａｌｔｅｓｔｉｎｇｏｎｔｈｅＵＣＦ －１０１ｄａｔａｓｅｔ．ＴｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｐｏｓｅｒｅｃｏｇｎｉｔｉｏｎａｎｄｂｅｈａｖｉｏｒｄｅｔｅｃｔｉｏｎｂａｓｅｄｏｎｔｈｅＳｌｏｗＦａｓｔｍｏｄｅｌ ｐｅｒｆｏｒｍｗｅｌｌｉｎｔｅｒｍｓｏｆａｃｃｕｒａｃｙ，ｒｅｃａｌｌ，ａｎｄＦ１ｓｃｏｒｅ，ｄｅｍｏｎｓｔｒａｔｉｎｇｔｈｅｓｙｓｔｅｍｓｅｆｆｅｃｔｉｖｅｎｅｓｓａｎｄｒｅ ｌｉａｂｉｌｉｔｙ．Ｔｈｅｃｏｎｃｌｕｓｉｏｎｉｎｄｉｃａｔｅｓｔｈａｔｔｈｅｉｎｔｅｌｌｉｇｅｎｔｂｕｉｌｄｉｎｇｓａｆｅｔｙｍｏｎｉｔｏｒｉｎｇｓｙｓｔｅｍ ｂａｓｅｄｏｎｅｄｇｅ ｃｏｍｐｕｔｉｎｇｐｒｏｐｏｓｅｄｉｎｔｈｉｓｓｔｕｄｙｅｆｆｅｃｔｉｖｅｌｙａｄｄｒｅｓｓｅｓｔｈｅｐｒｏｂｌｅｍｏｆｉｎｔｅｌｌｉｇｅｎｔｂｕｉｌｄｉｎｇｓａｆｅｔｙｍｏｎｉｔｏ ｒｉｎｇ，ｐｒｏｖｉｄｉｎｇｒｅｌｉａｂｌｅｔｅｃｈｎｉｃａｌｓｕｐｐｏｒｔｆｏｒｂｕｉｌｄｉｎｇｓａｆｅｔｙｍａｎａｇｅｍｅｎｔ．Ｉｎｔｈｅｆｕｔｕｒｅ，ａｌｇｏｒｉｔｈｍｓａｎｄ ｓｙｓｔｅｍａｒｃｈｉｔｅｃｔｕｒｅｃａｎｂｅｆｕｒｔｈｅｒｏｐｔｉｍｉｚｅｄｔｏｅｎｈａｎｃｅｓｙｓｔｅｍｐｅｒｆｏｒｍａｎｃｅａｎｄｒｅｌｉａｂｉｌｉｔｙ，ｍｅｅｔｉｎｇｔｈｅ ｇｒｏｗｉｎｇｄｅｍａｎｄｆｏｒｂｕｉｌｄｉｎｇｓａｆｅｔｙｍａｎａｇｅｍｅｎｔ
Key words: ｅｄｇｅｃｏｍｐｕｔｉｎｇ, ｖｉｄｅｏｓｕｒｖｅｉｌｌａｎｃｅ, Ｙｏｌｏｍｏｄｅｌ, ＳｌｏｗＦａｓｔｍｏｄｅｌ
CLC Number:
ＴＵ７１４
ＧＵＯＹｕｆｅｎｇａ, ＤＯＮＧＹａｊｉｅａ, ＬＩＹａｎａ, ＬＩＨａｏａ, ＷＡＮＧＮａｂ, ＷＡＮＧＬｉａｎｘｕｂ. ＩｎｔｅｌｌｉｇｅｎｔＢｕｉｌｄｉｎｇＳａｆｅｔｙＭｏｎｉｔｏｒｉｎｇＳｙｓｔｅｍ ＢａｓｅｄｏｎＥｄｇｅＣｏｍｐｕｔｉｎｇ[J]. HPMug2oMmNrOfxWQHLiEksa6s0hFu9Ox348d7QefarYlaFR5ArkhOwm3Da1pmxmxCtenj1+6luWD#r#n+EPn9L6Ce+9onqnMlT+i, 2024, 40(4): 89-.
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URL: http://kmyzxb.magtech.com.cn/EN/10.3969/j.issn.1009-0479.2024.04.015
http://kmyzxb.magtech.com.cn/EN/Y2024/V40/I4/89
