椤逛繆 (鍓暀鎺?
椤逛繆锛?/span>2008骞淬€?/span>2012骞存瘯涓氫簬涓崡姘戞棌澶у鐢靛瓙淇℃伅宸ョ▼瀛﹂櫌锛屽垎鍒幏瀛﹀＋鍜岀澹浣嶏紝2016骞存瘯涓氫簬鍗庝腑绉戞妧澶у浜哄伐鏅鸿兘涓庤嚜鍔ㄥ寲瀛﹂櫌锛岃幏鍗氬＋瀛︿綅銆?/span>2008骞?/span>7鏈堣嚦2008骞?/span>9鏈堬紝浠诲焹妫摬鍏徃鐮斿彂宸ョ▼甯堛€傝嚜2016骞?/span>8鏈堣捣鍦ㄤ腑鍗楁皯鏃忓ぇ瀛︾數瀛愪俊鎭伐绋嬪闄换鏁欙紝鐜颁负鏅鸿兘鏃犵嚎閫氫俊婀栧寳鐪侀噸鐐瑰疄楠屽鍔╃悊鏁欐巿銆佺澹敓瀵煎笀锛?/span>2019骞村吋鑱屾繁鍦冲競浜哄伐鏅鸿兘涓庢満鍣ㄤ汉鐮旂┒闄㈠搴х爺绌跺憳锛屼富瑕佷粠浜嬭绠楁満瑙嗚棰嗗煙鐨勭爺绌跺伐浣滐紝鍖呮嫭鐩爣妫€娴嬨€佸鐩爣璺熻釜銆佽涓哄垎鏋愩€佽棰戠洃鎺с€佹繁搴﹀涔犳妧鏈瓑銆傝繎5骞达紝涓绘寔鎴栧弬涓庡浗瀹堕噸鐐圭爺鍙戣鍒掗」鐩棰?/span>2椤癸紝鍥藉鑷劧绉戝鍩洪噾4椤广€佹箹鍖楃渷鑷劧绉戝鍩洪噾1椤广€備互绗竴浣滆€呭湪鍥介檯璁＄畻鏈鸿瑙変富娴佹湡鍒婂拰浼氳涓婂彂琛ㄨ鏂囧绡囥€?/span>
鍙戣〃鐨勮鏂?/span>
[1] jun xiang, guohan xu, chao ma, jianhua hou. 鈥淓nd-to-end learning deep crf models for multi-object tracking鈥?[j]. ieee transactions on circuits and systems for video technology, 2021,31(1):275-288.
[2] jun xiang, guoshuai zhang, jianhua hou. 鈥?online multi-object tracking based on feature representation and bayesian filtering within a deep learning architecture鈥?[j]. ieee access, 2019.
[3] jun xiang, guoshuai zhang, nong sang, rui huang, jianhua hou. 鈥?multiple object tracking by learning feature representation and distance metric jointly鈥?[c]. in: proceedings of british machine vision conference (bmvc), 2018.
[4] jun xiang, nong sang, jianhua hou, rui huang, changxin gao. 鈥淢ulti-target tracking using hough forest random field鈥?[j]. ieee transactions on circuits and systems for video technology, 2016锛?/span>26(11): 2028-2042.
[5] jun xiang, nong sang, jianhua hou, rui huang, changxin gao. 鈥淗ough forest based association framework with occlusion handling for multi-target tracking鈥?[j]. ieee signal processing letters, 2016, 23 (2): 257-261.
[6] jun xiang, ranran lin, jianhua hou, wenjun huang. 鈥?person re-identification based on feature fusion and triplet loss function鈥漑c].in: proceedings of ieee international conference on pattern recognition (icpr), 2018.
[7] jun xiang, nong sang, jianhua hou, rui huang, changxin gao. 鈥淒ata association based multi-target tracking using a joint formulation鈥?[c]. in: proceedings of asian conference on computer vision (accv), 2016.
[8] jun xiang , nong sang, jianhua hou, 鈥淎n online learned hough forest model for multi-target tracking鈥?[c]. in: proceedings of ieee international conference on image processing (icip), 2014: 2398-2403.
[9] 榛勫, 椤逛繆, 渚缓鍗?/span>, 寮犲崕, 绗偊鍙?/span>.鈥?/span>鑱斿悎鐗瑰緛铻嶅悎鍜屽垽鍒€у瑙傛ā鍨嬬殑澶氱洰鏍囪窡韪?/span>鈥?[j].涓浗鍥捐薄鍥惧舰瀛︽姤, 2015, 20 (9): 1188-1198.
[10]缃楄壋, 椤逛繆, 涓ユ槑鍚?/span>, 渚缓鍗?/span>. 鈥?/span>鍩轰簬澶氱ず渚嬪涔犲拰闅忔満鍘ヤ笡妫€娴嬬殑鍦ㄧ嚎鐩爣璺熻釜鈥?[j]. 鐢靛瓙涓庝俊鎭鎶ワ紝2014, 36(7): 1605-1611.
鎺堟潈涓撳埄
(1) 椤逛繆锛涚帇瓒咃紱渚缓鍗庯紱楹诲缓锛涘緪鍥藉瘨锛涗竴绉嶅熀浜庡浘鍖归厤鐨勫鐩爣璺熻釜鏂规硶鍜岀郴缁燂紝2020-07-17,涓浗 zl 202010689629.7
(2) 渚缓鍗庯紱楹诲缓锛涢」淇婏紱鐜嬭秴锛涙柟闀挎睙锛涗竴绉嶅熀浜庤嚜娉ㄦ剰鍔涙満鍒剁殑鍔ㄦ€佹墜鍔胯瘑鍒柟娉曞拰绯荤粺锛?/span>2020-06-29锛?/span>涓浗 zl 202010607626.4
(3) 椤逛繆; 寰愬浗瀵?/span>; 渚缓鍗?/span>; 涓€绉嶅熀浜庢繁搴︽潯浠堕殢鏈哄満妯″瀷鐨勫鐩爣璺熻釜鏂规硶鍜岀郴缁?/span>,2019-4-28, 涓浗, zl 2019010348058.8.
(4) 椤逛繆; 楹诲缓; 鐗熸捣鍐?/span>; 渚缓鍗?/span>; 涓€绉嶅熀浜庢墜鍔垮叧閿偣鑳″姩鎬佹墜鍔胯瘑鍒柟娉曞拰绯荤粺, 2018-7-23, 涓浗, zl 201810811365.0.
(5) 椤逛繆; 妗戝啘; 楂樺父閼?/span>; 闄堥椋?/span>; 鍐靛皬鐞?/span>; 鐜嬫鼎姘?/span>; 涓€绉嶅熀浜庡湪绾垮涔犺窡韪洃鎺ц棰戜腑澶氫釜杩愬姩鐩爣鐨勬柟娉?/span>, zl 2014 1 0333142.x.
(6) 渚缓鍗?/span>; 楹诲缓; 椤逛繆; 寮犲浗甯?/span>; 涓€绉嶅熀浜庣缁忕綉缁滅殑涓夌淮鎵嬪娍鍏抽敭鐐规娴嬫柟娉曞拰绯荤粺, zl 2018 1 0860857.9.
鑱旂郴鏂瑰紡
閭锛歫unxiang@scuec.edu.cn
鍦板潃锛氫腑鍗楁皯鏃忓ぇ瀛︾數瀛愪俊鎭伐绋嬪闄?/span>16鍙锋暀瀛︽ゼ锛?/span>10-1041/10-1045
鏈棰樼粍甯稿勾鍚告敹浼樼鏈鍚屽鍔犲叆锛屾杩庡悓瀛︿滑韪婅穬鎶ュ悕!
娆㈣繋鏈夊織闈掑勾鍔犲叆鏈棰樼粍鏀昏纭曞＋鐮旂┒鐢燂紒
