{"id":8924,"date":"2024-05-21T07:01:01","date_gmt":"2024-05-20T23:01:01","guid":{"rendered":""},"modified":"2024-05-21T07:01:01","modified_gmt":"2024-05-20T23:01:01","slug":"TraDes\u7b80\u8ff0","status":"publish","type":"post","link":"https:\/\/mushiming.com\/8924.html","title":{"rendered":"TraDes\u7b80\u8ff0"},"content":{"rendered":"

\n <\/path> \n<\/svg> <\/p>\n

\u5f15\u8a00<\/h3>\n

\u2003\u2003\u76ee\u524d\u5728\u7ebf\u591a\u76ee\u6807\u8ddf\u8e2a\u65b9\u6cd5\u4e3b\u8981\u9075\u5faa\u4e24\u4e2a\u6a21\u5f0f\uff08\u539f\u6587\u7528\u7684\u662fparadigm\uff08\u8303\u4f8b\uff0c\u6a21\u8303\uff09\uff0c\u5982\u679c\u5199\u82f1\u6587\u8bba\u6587\u7684\u8bdd\u4e5f\u8bb8\u7528\u5f97\u4e0a\uff09\uff1a\u5148\u68c0\u6d4b\u540e\u8ddf\u8e2a\uff08tracking-by-detection\uff0cTBD\uff09\u3001\u8054\u5408\u68c0\u6d4b\u8ddf\u8e2a\uff08joint detection and tracking\uff0cJDT\uff09\uff0c\u672c\u6587\u65b9\u6cd5\u662f\u9075\u5faa\u8054\u5408\u68c0\u6d4b\u8ddf\u8e2a\uff0c\u4e0d\u540c\u7684\u6a21\u5f0f\u5bf9\u6bd4\u5982\u4e0b\uff1a
\"TraDes\u7b80\u8ff0
\u2003\u2003\u5bf9\u6bd4DBT\u7684\u4e24\u4e2abackbone\uff0cJDT\u6709\u5929\u7136\u7684\u4f18\u52bf\uff0c\u4e00\u4e2a\u7f51\u7edc\u540c\u65f6\u5b9e\u73b0\u68c0\u6d4b\u548c\u5173\u8054\u4efb\u52a1\uff0c\u80fd\u66f4\u52a0\u5feb\u901f\u7684\u8fdb\u884c\u63a8\u7406\u3002JDT\u867d\u7136\u7f51\u7edc\u5171\u4eab\uff0c\u826f\u597d\u7684\u68c0\u6d4b\u5bf9\u4e8e\u8ffd\u8e2a\u7684\u7a33\u5b9a\u548c\u4e00\u81f4\u662f\u6709\u76ca\u7684\uff0c\u4f46\u662f\u76f8\u8f83\u4e8e\u8ffd\u8e2a\u540e\u7684\u7ed3\u679c\uff0c\u68c0\u6d4b\u4ecd\u7136\u662f\u72ec\u7acb\u7684\uff0c\u4e5f\u5c31\u662f\u8ffd\u8e2a\u540e\u5f97\u5230\u7684\u7ed3\u679c\u5bf9\u4e0b\u6b21\u68c0\u6d4b\u51e0\u4e4e\u65e0\u4efb\u4f55\u5173\u7cfb\u3002\u5e76\u4e14\u5176\u4e2d\u4f7f\u7528\u7684re-ID\u635f\u5931\u5f80\u5f80\u4e0d\u5229\u4e8e\u68c0\u6d4b\u6548\u679c\u7684\u63d0\u5347\uff0cre-ID\u7684\u635f\u5931\u5173\u6ce8\u7684\u662f\u7c7b\u5185\u7684\u53d8\u91cf\uff0c\u9700\u8981\u5c3d\u53ef\u80fd\u7684\u5c06\u7c7b\u5185\u62c9\u5f00\uff0c\u800c\u68c0\u6d4b\u7684\u76ee\u7684\u662f\u6700\u5927\u5316\u7c7b\u95f4\u7684\u533a\u522b\uff0c\u6700\u5c0f\u5316\u7c7b\u5185\u7684\u53d8\u91cf\u3002
\u2003\u2003\u9762\u5bf9\u4ee5\u4e0a\u95ee\u9898\uff0c\u4f5c\u8005\u8ba4\u4e3a\u4f7f\u7528\u8ffd\u8e2a\u7684\u7ebf\u7d22\u5e94\u8be5\u6765\u5e2e\u52a9\u68c0\u6d4b\uff0c\u4e8e\u662f\u63d0\u51fa\u4e86TraDeS\u6a21\u578b\uff0c\u6a21\u578b\u4e2d\u7684\u7279\u5f81\u56fe\u4e2d\u7684\u6bcf\u4e2a\u70b9\u4ee3\u8868\u4e00\u4e2a\u76ee\u6807\u4e2d\u5fc3\u6216\u8005\u80cc\u666f\u533a\u57df\uff0c\u7c7b\u4f3c\u4e8eCenterNet\uff0c\u901a\u8fc7\u5c06\u8ffd\u8e2a\u6574\u5408\u8fdb\u68c0\u6d4b\u7684\u540c\u65f6\uff0c\u4e13\u95e8\u8bbe\u8ba1\u4e86\u4e00\u4e2are-ID\u5b66\u4e60\u673a\u5236\u6765\u89e3\u51b3\u95ee\u9898\uff0c\u7136\u540e\u63d0\u51fa\u4e86\u4e24\u4e2a\u6a21\u5757\uff1a\u57fa\u4e8e\u4ee3\u4ef7\u91cf\u7684\u5173\u8054(CVA)\u6a21\u5757\u548c\u4e00\u4e2a\u8fd0\u52a8\u5f15\u5bfc\u7279\u5f81\u6574\u7406(MFW)\u6a21\u5757\u3002<\/strong>
\u2003\u2003CVA\u6a21\u5757\u901a\u8fc7backbone\u63d0\u53d6\u70b9\u7ea7re-ID embedding\u7279\u5f81\u6765\u6784\u5efa\u4e00\u4e2a\u5b58\u50a8\u4e24\u5e27\u95f4\u7684\u6240\u6709embedding\u5bf9\u4e4b\u95f4\u7684\u5339\u914d\u76f8\u4f3c\u5ea6\u7684\u4ee3\u4ef7\u5411\u91cf\uff08cost volume\uff09\u3002\u5229\u7528\u4ee3\u4ef7\u5411\u91cf\uff0c\u63a8\u7406\u51fa\u8ffd\u8e2a\u7684\u504f\u79fb\uff08\u6240\u6709\u70b9\u7684\u65f6\u7a7a\u4f4d\u79fb\uff0c\u5982\u6f5c\u5728\u76ee\u6807\u7684\u4e2d\u5fc3\u70b9\u5728\u4e24\u5e27\u4e2d\u4f4d\u79fb\uff09\u3002\u8fd9\u4e9b\u8ffd\u8e2a\u504f\u79fb\u52a0\u4e0a\u63d0\u53d6\u7684embedding\u4e00\u8d77\u6784\u5efa\u6570\u636e\u5173\u8054\u3002\u4e4b\u540eMKF\u5229\u7528\u8ffd\u8e2a\u504f\u79fb\u4f5c\u4e3a\u8fd0\u52a8\u7ebf\u7d22\uff0c\u5c06\u5bf9\u8c61\u7279\u5f81\u4ece\u524d\u4e00\u5e27\u4f20\u64ad\u5230\u5f53\u524d\u5e27\uff0c\u6700\u7ec8\u4f20\u64ad\u7684\u7279\u5f81\u548c\u5f53\u524d\u7684\u7279\u5f81\u4e00\u8d77\u805a\u96c6\u6765\u5b9e\u73b0\u68c0\u6d4b\u548c\u5206\u5272\u3002
\u2003\u2003\u5728CVA\u4e2d\uff0cre-ID objective\u6d89\u53ca\u7c7b\u95f4\u65b9\u5dee\uff0c\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u5b66\u4e60\u4e86\u6709\u6548\u7684embedding\u4f5c\u4e3a\u5e38\u89c1\u7684re-ID\u635f\u5931\uff0c\u800c\u4e14\u4e0e\u68c0\u6d4b\u635f\u5931\u5f88\u597d\u5730\u517c\u5bb9\uff0c\u4e0d\u4f1a\u635f\u5bb3\u68c0\u6d4b\u6027\u80fd\uff0c\u5982\u4e0b\u9762\u88681-b\u6240\u793a\u3002\u6b64\u5916\uff0c\u56e0\u4e3a\u8ddf\u8e2a\u504f\u79fb\u662f\u57fa\u4e8e\u5916\u89c2embedding\u76f8\u4f3c\u6027\u6765\u9884\u6d4b\u7684\uff0c\u6240\u4ee5\u5b83\u53ef\u4ee5\u5339\u914d\u5177\u6709\u975e\u5e38\u5927\u7684\u8fd0\u52a8\u6216\u5904\u4e8e\u4f4e\u5e27\u901f\u7387\u7684\u5bf9\u8c61\uff0c\u5982\u56fe3\u6240\u793a\uff0c\u6216\u8005\u751a\u81f3\u7cbe\u786e\u5730\u8ddf\u8e2a\u5177\u6709\u4e0d\u53ef\u89c1\u7684\u5927\u8fd0\u52a8\u7684\u4e0d\u540c\u6570\u636e\u96c6\u4e2d\u7684\u5bf9\u8c61\uff0c\u5982\u56fe4\u6240\u793a\u3002
\"TraDes\u7b80\u8ff0<\/p>\n

\"TraDes\u7b80\u8ff0
\"TraDes\u7b80\u8ff0<\/p>\n

\u76f8\u5173\u5de5\u4f5c<\/h3>\n

\u2003\u2003Tracking-by-Detection<\/strong>\uff1a\u5728\u8fd9\u4e2a\u6846\u67b6\u5185\uff0c\u9996\u5148\u5e94\u7528\u73b0\u6210\u7684\u5bf9\u8c61\u68c0\u6d4b\u5668\u6765\u4e3a\u6bcf\u4e2a\u5e27\u751f\u6210\u68c0\u6d4b\u6846\u3002\u7136\u540e\uff0c\u4f7f\u7528\u5355\u72ec\u7684\u91cd\u65b0\u6807\u8bc6\u6a21\u578b\u6765\u63d0\u53d6\u90a3\u4e9b\u68c0\u6d4b\u5230\u7684box\u7684\u5916\u89c2\u7279\u5f81\u3002\u4e3a\u4e86\u6784\u5efa\u8f68\u8ff9\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u89e3\u51b3\u65b9\u6848\u662f\u76f4\u63a5\u8ba1\u7b97\u4e0e\u8fd0\u52a8\u6a21\u578b(\u4f8b\u5982\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668)\u7684\u5916\u89c2\u548c\u8fd0\u52a8\u76f8\u4f3c\u6027\uff0c\u7136\u540e\u901a\u8fc7\u5339\u914d\u7b97\u6cd5\u6765\u89e3\u51b3\u6570\u636e\u5173\u8054\u3002\u5176\u4ed6\u4e00\u4e9b\u52aa\u529b\uff08\u300aLearning a neural solver for multiple object tracking\u300b\u3001\u300aMultiple target tracking based on undirected hierarchical relation hypergraph\u300b\u3001\u300aMultiple hypothesis tracking revisited\u300b\uff09\uff0c\u901a\u8fc7\u5c06\u6bcf\u4e2a\u68c0\u6d4b\u89c6\u4e3a\u56fe\u8282\u70b9\uff0c\u5c06\u6570\u636e\u5173\u8054\u516c\u5f0f\u5316\u4e3a\u56fe\u4f18\u5316\u95ee\u9898\u3002\u7136\u800c\uff0cTBD\u65b9\u6cd5\u5206\u522b\u8fdb\u884c\u68c0\u6d4b\u548c\u8ddf\u8e2a\uff0c\u56e0\u6b64\u901a\u5e38\u8ba1\u7b97\u91cf\u5f88\u5927\u3002\u672c\u6587\u65b9\u6cd5\u5c06\u8ddf\u8e2a\u7ebf\u7d22\u96c6\u6210\u5230\u68c0\u6d4b\u4e2d\uff0c\u5e76\u4ee5\u7aef\u5230\u7aef\u7684\u65b9\u5f0f\u9ad8\u6548\u5730\u6267\u884c\u68c0\u6d4b\u548c\u8ddf\u8e2a\u3002
\u2003\u2003Joint Detection and Tracking<\/strong>\uff1a\u5e38\u89c1\u7684\u65b9\u6cd5\uff08\u300aTracking objects as points\u300b\u3001\u300aTowards real-time multi-object tracking\u300b\u3001\u300aOnline single stage joint detection and tracking\u300b\u3001\u300aTracking without bells and whistles\u300b\u3001\u300aIntegrated object detection and tracking with tracklet-conditioned detection.\u300b\u3001\u300aFairmot: On the fairness of detection and re-identification in multiple object tracking.\u300b\uff09\u662f\u5728\u5bf9\u8c61\u68c0\u6d4b\u5668\u4e0a\u5efa\u7acb\u4e00\u4e2a\u4e0e\u8ddf\u8e2a\u76f8\u5173\u7684\u5206\u652f\uff0c\u4ee5\u9884\u6d4b\u5bf9\u8c61\u8ddf\u8e2a\u504f\u79fb\u6216\u6570\u636e\u5173\u8054\u7684re-ID embedding\u3002\u6216\u8005\uff0c\u5229\u7528transformer\u6765\u5339\u914d\u8f68\u8ff9\uff08\u300aTranstrack: Multiple-object tracking with transformer.\u300b\u3001\u300aTrackformer: Multi-object tracking with transformers.\u300b\uff09\u3002CTracker\u901a\u8fc7\u5728\u6bcf\u4e24\u5e27\u4e2d\u94fe\u63a5\u6210\u5bf9\u7684\u6846\u6765\u6784\u5efa\u8f68\u8ff9\u3002TubeTK\u4ee5\u79bb\u7ebf\u65b9\u5f0f\u76f4\u63a5\u9884\u6d4b\u4f5c\u4e3a\u8f68\u8ff9\u7684\u76d2\u5f62\u7ba1\u3002\u7136\u800c\uff0c\u5927\u591a\u6570JDT\u65b9\u6cd5\u9762\u4e34\u4e24\u4e2a\u95ee\u9898:\u7b2c\u4e00\uff0c\u6ca1\u6709\u8ddf\u8e2a\u7684\u5e2e\u52a9\uff0c\u68c0\u6d4b\u4ecd\u7136\u662f\u5355\u72ec\u9884\u6d4b\u7684\u3002\u7b2c\u4e8c\uff0cre-ID\u635f\u5931\u4e0e\u8054\u5408\u8bad\u7ec3\u4e2d\u7684\u68c0\u6d4b\u635f\u5931\u5177\u6709\u4e0d\u540c\u7684\u76ee\u6807\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u6211\u672c\u6587\u65b9\u6cd5\u5c06\u8ddf\u8e2a\u7ebf\u7d22\u7d27\u5bc6\u5730\u7ed3\u5408\u5230\u68c0\u6d4b\u4e2d\u5e76\u8bbe\u8ba1\u4e00\u79cd\u65b0\u7684re-ID embedding\u5b66\u4e60\u65b9\u6848\u6765\u89e3\u51b3\u8fd9\u4e24\u4e2a\u95ee\u9898\u3002
\u2003\u2003Tracking-guided Video Object Detection<\/strong>\uff1a\u5728\u89c6\u9891\u5bf9\u8c61\u68c0\u6d4b\u4e2d\uff0c\u4e00\u4e9b\u5c1d\u8bd5\uff08\u300aDetect to track and track to detect\u300b\u3001\u300aIntegrated object detection and tracking with tracklet-conditioned detection.\u300b\uff09\u5229\u7528\u8ddf\u8e2a\u7ed3\u679c\u6765\u91cd\u65b0\u52a0\u6743\u7531\u521d\u59cb\u68c0\u6d4b\u5668\u751f\u6210\u7684\u68c0\u6d4b\u5206\u6570\u3002\u867d\u7136\u8fd9\u4e9b\u5de5\u4f5c\u52aa\u529b\u901a\u8fc7\u8ddf\u8e2a\u6765\u5e2e\u52a9\u68c0\u6d4b\uff0c\u4f46\u5b83\u4eec\u6709\u4e24\u4e2a\u7f3a\u70b9:\u9996\u5148\uff0c\u8ddf\u8e2a\u4ec5\u5728\u540e\u5904\u7406\u9636\u6bb5\u88ab\u7528\u6765\u5e2e\u52a9\u68c0\u6d4b\u3002\u68c0\u6d4b\u4ecd\u7136\u7531\u72ec\u7acb\u7684\u5bf9\u8c61\u68c0\u6d4b\u5668\u9884\u6d4b\uff0c\u56e0\u6b64\u68c0\u6d4b\u548c\u8ddf\u8e2a\u662f\u5355\u72ec\u4f18\u5316\u7684\u3002\u56e0\u6b64\uff0c\u6700\u7ec8\u7684\u68c0\u6d4b\u5206\u6570\u53ef\u80fd\u4e25\u91cd\u4f9d\u8d56\u4e8e\u8ddf\u8e2a\u8d28\u91cf\u3002\u5176\u6b21\uff0c\u624b\u5de5\u5236\u4f5c\u7684\u91cd\u65b0\u52a0\u6743\u65b9\u6848\u9700\u8981\u9488\u5bf9\u7279\u5b9a\u7684\u68c0\u6d4b\u5668\u548c\u8ddf\u8e2a\u5668\u8fdb\u884c\u624b\u52a8\u8c03\u6574\u3002\u672c\u6587\u68c0\u6d4b\u662f\u5728\u8ddf\u8e2a\u7ed3\u679c\u7684\u6761\u4ef6\u4e0b\u5b66\u4e60\u7684\u3002
\u2003\u2003Cost Volume<\/strong>\uff1acost volume\u5df2\u7ecf\u6210\u529f\u5730\u5e94\u7528\u4e8e\u6df1\u5ea6\u4f30\u8ba1\u548c\u5149\u6d41\u4f30\u8ba1\uff0c\u7528\u4e8e\u5173\u8054\u4e24\u4e2a\u5e27\u4e4b\u95f4\u7684\u50cf\u7d20\u3002\u672c\u6587\u5c06cost volume\u6269\u5c55\u5230\u591a\u76ee\u6807\u8ddf\u8e2a\u5668\uff0c\u672c\u6587\u5c06\u8bc1\u660e\u5728\u5b66\u4e60re-ID embedding\u548c\u63a8\u65ad\u8ddf\u8e2a\u504f\u79fb\u65b9\u9762\u662f\u6709\u6548\u7684\u3002<\/p>\n

\u65b9\u6cd5<\/h3>\n

\u2003\u2003TraDeS\u57fa\u4e8e\u57fa\u4e8e\u70b9\u7684\u68c0\u6d4b\u5668CenterNet\uff08\u300aObjects as Points\u300b\uff09\u8fdb\u884c\u6784\u5efa\u7684\uff0c\u7c7b\u4f3c\u4e8e\uff08\u300aTracking objects as points.\u300b\uff09\uff0c\u5728CenterNet\u4e0a\u6dfb\u52a0\u4e00\u4e2a\u989d\u5916\u7684\u5206\u652f\u9884\u6d4b\u8ddf\u8e2a\u7528\u4e8e\u6570\u636e\u5173\u8054\u3002\u5927\u591a\u6570\u4ee5\u524d\u7684\u8054\u5408\u68c0\u6d4b\u548c\u8ddf\u8e2a\u65b9\u6cd5\u5728\u6ca1\u6709\u6765\u81ea\u8ddf\u8e2a\u7684\u660e\u786e\u8f93\u5165\u7684\u60c5\u51b5\u4e0b\u6267\u884c\u72ec\u7acb\u68c0\u6d4b\u3002
\u2003\u2003\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u672c\u6587\u76ee\u6807\u662f\u5c06\u8ddf\u8e2a\u7ebf\u7d22\u96c6\u6210\u5230\u7aef\u5230\u7aef\u7684\u68c0\u6d4b\u4e2d\uff0c\u4ece\u800c\u63d0\u9ad8\u5bf9\u56f0\u96be\u573a\u666f\u7684\u68c0\u6d4b\uff0c\u8fd9\u53cd\u8fc7\u6765\u53c8\u6709\u5229\u4e8e\u8ddf\u8e2a\u3002TraDes\u7684\u6846\u67b6\u5982\u4e0b\u6240\u793a\uff1a
\"TraDes\u7b80\u8ff0
CVA\u6a21\u5757<\/strong>\uff1a
\"TraDes\u7b80\u8ff0
\"TraDes\u7b80\u8ff0
MFW\u6a21\u5757<\/strong>\uff1a
\"TraDes\u7b80\u8ff0
Tracklet \u751f\u6210<\/strong>\uff1a
\"TraDes\u7b80\u8ff0<\/p>\n

\u5b9e\u9a8c<\/h3>\n

\u2003\u2003\u6d88\u878d\u5b9e\u9a8c
\"TraDes\u7b80\u8ff0
\u2003\u2003MOT Challenge 2D\u8ffd\u8e2a\u6570\u636e\u96c6\u5b9e\u9a8c\uff1a
\"TraDes\u7b80\u8ff0
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\u603b\u7ed3<\/h3>\n

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