diff --git a/media/RESULTS.md b/media/RESULTS.md
index b88d55a..226a858 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -7,9 +7,9 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:20)
 ```json
 {
-  "avg_time_2d": 0.0706314975932493,
-  "avg_time_3d": 0.002891659332534014,
-  "avg_fps": 13.6011569934277
+  "avg_time_2d": 0.07194638131028515,
+  "avg_time_3d": 0.0031490806805885446,
+  "avg_fps": 13.316383886439496
 }
 {
   "person_nums": {
@@ -30,7 +30,7 @@ Results of the model in various experiments on different datasets.
     "count": 600,
     "mean": 0.066878,
     "median": 0.059221,
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+    "std": 0.027763,
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@@ -42,23 +42,23 @@ Results of the model in various experiments on different datasets.
     "recall-0.5": 1.0,
     "num_labels": 600,
     "ap-0.025": 0.0,
-    "ap-0.05": 0.00225,
-    "ap-0.1": 0.895105,
-    "ap-0.15": 0.917278,
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+    "ap-0.1": 0.895107,
+    "ap-0.15": 0.917238,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
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   "nose": {
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-    "sem": 0.001747,
+    "mean": 0.117653,
+    "median": 0.101455,
+    "std": 0.042711,
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     "min": 0.046466,
     "max": 0.271134,
     "recall-0.025": 0.0,
     "recall-0.05": 0.003333,
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     "recall-0.25": 0.995,
     "recall-0.5": 1.0,
@@ -66,9 +66,9 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 600,
-    "mean": 0.033334,
+    "mean": 0.033344,
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@@ -82,9 +82,9 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 600,
-    "mean": 0.048002,
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-    "std": 0.04254,
+    "mean": 0.048035,
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@@ -98,14 +98,14 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
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-    "mean": 0.042568,
+    "mean": 0.042537,
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     "min": 0.002682,
     "max": 0.193558,
     "recall-0.025": 0.273333,
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     "recall-0.15": 0.956667,
     "recall-0.25": 1.0,
@@ -114,14 +114,14 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 600,
-    "mean": 0.04321,
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     "recall-0.025": 0.251667,
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@@ -130,12 +130,12 @@ Results of the model in various experiments on different datasets.
   },
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+    "std": 0.04223,
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@@ -146,14 +146,14 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
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-    "mean": 0.042567,
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@@ -162,9 +162,9 @@ Results of the model in various experiments on different datasets.
   },
   "hip_left": {
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@@ -178,10 +178,10 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
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@@ -194,9 +194,9 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
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@@ -210,14 +210,14 @@ Results of the model in various experiments on different datasets.
   },
   "knee_right": {
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@@ -226,7 +226,7 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_left": {
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@@ -242,9 +242,9 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
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@@ -259,8 +259,8 @@ Results of the model in various experiments on different datasets.
   "joint_recalls": {
     "num_labels": 7800,
     "recall-0.025": 0.17962,
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     "recall-0.25": 0.99397,
     "recall-0.5": 0.99974
@@ -278,33 +278,33 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:56)
 ```json
 {
-  "avg_time_2d": 0.10373067446181045,
-  "avg_time_3d": 0.01558385436067876,
-  "avg_fps": 8.381208976551004
+  "avg_time_2d": 0.10030525082984741,
+  "avg_time_3d": 0.00994173521848069,
+  "avg_fps": 9.07054274990917
 }
 {
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     "total_labels": 477,
-    "total_preds": 909,
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     "considered_empty": 3,
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     "missing": 0,
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     "recall": 1.0,
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@@ -313,23 +313,23 @@ Results of the model in various experiments on different datasets.
     "recall-0.5": 1.0,
     "num_labels": 477,
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@@ -353,13 +353,13 @@ Results of the model in various experiments on different datasets.
   },
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@@ -369,30 +369,30 @@ Results of the model in various experiments on different datasets.
   },
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@@ -401,10 +401,10 @@ Results of the model in various experiments on different datasets.
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@@ -417,9 +417,9 @@ Results of the model in various experiments on different datasets.
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@@ -433,31 +433,31 @@ Results of the model in various experiments on different datasets.
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@@ -465,14 +465,14 @@ Results of the model in various experiments on different datasets.
   },
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@@ -481,15 +481,15 @@ Results of the model in various experiments on different datasets.
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@@ -497,10 +497,10 @@ Results of the model in various experiments on different datasets.
   },
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@@ -513,10 +513,10 @@ Results of the model in various experiments on different datasets.
   },
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@@ -529,18 +529,18 @@ Results of the model in various experiments on different datasets.
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 }
 {
   "total_parts": 6678,
-  "correct_parts": 6615,
-  "pcp": 0.990566
+  "correct_parts": 6618,
+  "pcp": 0.991015
 }
 ```
 
@@ -549,53 +549,53 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:29)
 ```json
 {
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+  "avg_time_3d": 0.0035331103036988455,
+  "avg_fps": 16.56781908577916
 }
 {
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   "head": {
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@@ -608,13 +608,13 @@ Results of the model in various experiments on different datasets.
   },
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@@ -624,10 +624,10 @@ Results of the model in various experiments on different datasets.
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@@ -640,14 +640,14 @@ Results of the model in various experiments on different datasets.
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@@ -656,10 +656,10 @@ Results of the model in various experiments on different datasets.
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@@ -672,10 +672,10 @@ Results of the model in various experiments on different datasets.
   },
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@@ -688,31 +688,31 @@ Results of the model in various experiments on different datasets.
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@@ -720,15 +720,15 @@ Results of the model in various experiments on different datasets.
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@@ -736,82 +736,82 @@ Results of the model in various experiments on different datasets.
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+  "correct_parts": 4969,
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 }
 ```
 
@@ -820,189 +820,189 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:31)
 ```json
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@@ -1011,18 +1011,18 @@ Results of the model in various experiments on different datasets.
   "ankle_right": {},
   "joint_recalls": {
     "num_labels": 9549,
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 }
 {
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-  "correct_parts": 5861,
-  "pcp": 0.552403
+  "correct_parts": 5510,
+  "pcp": 0.519321
 }
 ```
 
@@ -1031,72 +1031,72 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:28)
 ```json
 {
-  "avg_time_2d": 0.10983607013051103,
-  "avg_time_3d": 0.023344272520483995,
-  "avg_fps": 7.508615611693869
+  "avg_time_2d": 0.11225916583363603,
+  "avg_time_3d": 0.012731300912252287,
+  "avg_fps": 8.000610174798759
 }
 {
   "person_nums": {
     "total_frames": 420,
     "total_labels": 1466,
-    "total_preds": 1568,
-    "considered_empty": 8,
+    "total_preds": 1509,
+    "considered_empty": 5,
     "valid_preds": 1463,
-    "invalid_preds": 97,
+    "invalid_preds": 41,
     "missing": 3,
-    "invalid_fraction": 0.06218,
-    "precision": 0.93782,
+    "invalid_fraction": 0.02726,
+    "precision": 0.97274,
     "recall": 0.99795,
-    "f1": 0.96695,
-    "non_empty": 1560
+    "f1": 0.98519,
+    "non_empty": 1504
   },
   "mpjpe": {
     "count": 1463,
-    "mean": 0.044122,
-    "median": 0.0376,
-    "std": 0.024349,
-    "sem": 0.000637,
+    "mean": 0.044136,
+    "median": 0.038064,
+    "std": 0.023929,
+    "sem": 0.000626,
     "min": 0.01199,
     "max": 0.383741,
-    "recall-0.025": 0.162347,
-    "recall-0.05": 0.712824,
-    "recall-0.1": 0.96794,
-    "recall-0.15": 0.995907,
+    "recall-0.025": 0.158254,
+    "recall-0.05": 0.712142,
+    "recall-0.1": 0.969304,
+    "recall-0.15": 0.996589,
     "recall-0.25": 0.997271,
     "recall-0.5": 0.997954,
     "num_labels": 1466,
-    "ap-0.025": 0.063858,
-    "ap-0.05": 0.642449,
-    "ap-0.1": 0.949904,
-    "ap-0.15": 0.982339,
-    "ap-0.25": 0.983697,
-    "ap-0.5": 0.984345
+    "ap-0.025": 0.061059,
+    "ap-0.05": 0.64118,
+    "ap-0.1": 0.951321,
+    "ap-0.15": 0.983556,
+    "ap-0.25": 0.984229,
+    "ap-0.5": 0.9849
   },
   "nose": {
     "count": 1461,
-    "mean": 0.018639,
-    "median": 0.012435,
-    "std": 0.032832,
-    "sem": 0.000859,
+    "mean": 0.018933,
+    "median": 0.01246,
+    "std": 0.033585,
+    "sem": 0.000879,
     "min": 0.00163,
     "max": 0.316534,
-    "recall-0.025": 0.894053,
-    "recall-0.05": 0.955571,
-    "recall-0.1": 0.985646,
-    "recall-0.15": 0.986329,
-    "recall-0.25": 0.986329,
+    "recall-0.025": 0.891319,
+    "recall-0.05": 0.952837,
+    "recall-0.1": 0.984962,
+    "recall-0.15": 0.985646,
+    "recall-0.25": 0.985646,
     "recall-0.5": 0.998633,
     "num_labels": 1463
   },
   "shoulder_left": {
     "count": 1463,
-    "mean": 0.021907,
-    "median": 0.019325,
-    "std": 0.012953,
-    "sem": 0.000339,
+    "mean": 0.021887,
+    "median": 0.019127,
+    "std": 0.013093,
+    "sem": 0.000342,
     "min": 0.002677,
     "max": 0.124087,
-    "recall-0.025": 0.714188,
+    "recall-0.025": 0.715553,
     "recall-0.05": 0.967258,
     "recall-0.1": 0.993861,
     "recall-0.15": 0.997954,
@@ -1106,15 +1106,15 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 1462,
-    "mean": 0.023838,
-    "median": 0.019815,
-    "std": 0.016961,
-    "sem": 0.000444,
+    "mean": 0.023863,
+    "median": 0.019858,
+    "std": 0.016912,
+    "sem": 0.000442,
     "min": 0.002471,
     "max": 0.146597,
-    "recall-0.025": 0.690102,
+    "recall-0.025": 0.688055,
     "recall-0.05": 0.94198,
-    "recall-0.1": 0.988396,
+    "recall-0.1": 0.989078,
     "recall-0.15": 0.997952,
     "recall-0.25": 0.997952,
     "recall-0.5": 0.997952,
@@ -1122,15 +1122,15 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 1462,
-    "mean": 0.024992,
-    "median": 0.017082,
-    "std": 0.027098,
+    "mean": 0.025007,
+    "median": 0.017079,
+    "std": 0.027085,
     "sem": 0.000709,
     "min": 0.001846,
     "max": 0.323155,
-    "recall-0.025": 0.720137,
-    "recall-0.05": 0.891468,
-    "recall-0.1": 0.98157,
+    "recall-0.025": 0.720819,
+    "recall-0.05": 0.89215,
+    "recall-0.1": 0.980887,
     "recall-0.15": 0.991809,
     "recall-0.25": 0.994539,
     "recall-0.5": 0.997952,
@@ -1138,63 +1138,63 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 1461,
-    "mean": 0.024015,
+    "mean": 0.024025,
     "median": 0.01806,
-    "std": 0.01946,
-    "sem": 0.000509,
+    "std": 0.019691,
+    "sem": 0.000515,
     "min": 0.001407,
     "max": 0.233543,
-    "recall-0.025": 0.681476,
-    "recall-0.05": 0.917977,
-    "recall-0.1": 0.991798,
-    "recall-0.15": 0.996582,
+    "recall-0.025": 0.683527,
+    "recall-0.05": 0.920711,
+    "recall-0.1": 0.991114,
+    "recall-0.15": 0.995899,
     "recall-0.25": 0.998633,
     "recall-0.5": 0.998633,
     "num_labels": 1463
   },
   "wrist_left": {
     "count": 1429,
-    "mean": 0.035263,
-    "median": 0.016885,
-    "std": 0.051671,
-    "sem": 0.001367,
-    "min": 0.001303,
+    "mean": 0.035061,
+    "median": 0.016859,
+    "std": 0.051336,
+    "sem": 0.001359,
+    "min": 0.001333,
     "max": 0.496738,
-    "recall-0.025": 0.666667,
-    "recall-0.05": 0.822873,
-    "recall-0.1": 0.912134,
-    "recall-0.15": 0.958856,
+    "recall-0.025": 0.665969,
+    "recall-0.05": 0.82357,
+    "recall-0.1": 0.912831,
+    "recall-0.15": 0.960251,
     "recall-0.25": 0.987448,
     "recall-0.5": 0.996513,
     "num_labels": 1434
   },
   "wrist_right": {
     "count": 1454,
-    "mean": 0.026128,
-    "median": 0.017008,
-    "std": 0.026801,
-    "sem": 0.000703,
+    "mean": 0.026296,
+    "median": 0.017106,
+    "std": 0.028279,
+    "sem": 0.000742,
     "min": 0.001042,
-    "max": 0.202973,
-    "recall-0.025": 0.683379,
-    "recall-0.05": 0.880495,
-    "recall-0.1": 0.963599,
-    "recall-0.15": 0.991071,
-    "recall-0.25": 0.998626,
+    "max": 0.394822,
+    "recall-0.025": 0.681319,
+    "recall-0.05": 0.879808,
+    "recall-0.1": 0.962912,
+    "recall-0.15": 0.992445,
+    "recall-0.25": 0.99794,
     "recall-0.5": 0.998626,
     "num_labels": 1456
   },
   "hip_left": {
     "count": 1461,
-    "mean": 0.058624,
-    "median": 0.052631,
-    "std": 0.029266,
-    "sem": 0.000766,
+    "mean": 0.058879,
+    "median": 0.052826,
+    "std": 0.029358,
+    "sem": 0.000768,
     "min": 0.004253,
     "max": 0.17979,
     "recall-0.025": 0.070307,
-    "recall-0.05": 0.445051,
-    "recall-0.1": 0.895563,
+    "recall-0.05": 0.440273,
+    "recall-0.1": 0.894198,
     "recall-0.15": 0.974061,
     "recall-0.25": 0.99727,
     "recall-0.5": 0.99727,
@@ -1202,15 +1202,15 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 1462,
-    "mean": 0.060147,
-    "median": 0.057418,
-    "std": 0.02837,
-    "sem": 0.000742,
+    "mean": 0.060436,
+    "median": 0.057528,
+    "std": 0.028518,
+    "sem": 0.000746,
     "min": 0.010398,
     "max": 0.304524,
-    "recall-0.025": 0.045703,
-    "recall-0.05": 0.379263,
-    "recall-0.1": 0.912005,
+    "recall-0.025": 0.046385,
+    "recall-0.05": 0.375853,
+    "recall-0.1": 0.906548,
     "recall-0.15": 0.987722,
     "recall-0.25": 0.996589,
     "recall-0.5": 0.997271,
@@ -1218,82 +1218,82 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 1461,
-    "mean": 0.05085,
-    "median": 0.04337,
-    "std": 0.038015,
-    "sem": 0.000995,
+    "mean": 0.050771,
+    "median": 0.043452,
+    "std": 0.03768,
+    "sem": 0.000986,
     "min": 0.001938,
     "max": 0.349881,
-    "recall-0.025": 0.178157,
-    "recall-0.05": 0.619113,
+    "recall-0.025": 0.17884,
+    "recall-0.05": 0.614334,
     "recall-0.1": 0.936519,
-    "recall-0.15": 0.971331,
+    "recall-0.15": 0.972696,
     "recall-0.25": 0.988396,
     "recall-0.5": 0.99727,
     "num_labels": 1465
   },
   "knee_right": {
     "count": 1456,
-    "mean": 0.050085,
-    "median": 0.04265,
-    "std": 0.035134,
-    "sem": 0.000921,
+    "mean": 0.049844,
+    "median": 0.042843,
+    "std": 0.034357,
+    "sem": 0.000901,
     "min": 0.000605,
     "max": 0.480558,
-    "recall-0.025": 0.189856,
+    "recall-0.025": 0.186429,
     "recall-0.05": 0.590816,
-    "recall-0.1": 0.921864,
+    "recall-0.1": 0.925977,
     "recall-0.15": 0.986292,
-    "recall-0.25": 0.991775,
+    "recall-0.25": 0.992461,
     "recall-0.5": 0.997944,
     "num_labels": 1459
   },
   "ankle_left": {
-    "count": 1448,
-    "mean": 0.08721,
-    "median": 0.035703,
-    "std": 0.109209,
-    "sem": 0.002871,
+    "count": 1449,
+    "mean": 0.087757,
+    "median": 0.035935,
+    "std": 0.108924,
+    "sem": 0.002862,
     "min": 0.002453,
-    "max": 0.495773,
-    "recall-0.025": 0.376623,
-    "recall-0.05": 0.578264,
-    "recall-0.1": 0.712235,
-    "recall-0.15": 0.789474,
+    "max": 0.498193,
+    "recall-0.025": 0.375256,
+    "recall-0.05": 0.572796,
+    "recall-0.1": 0.708134,
+    "recall-0.15": 0.788107,
     "recall-0.25": 0.900205,
-    "recall-0.5": 0.989747,
+    "recall-0.5": 0.990431,
     "num_labels": 1463
   },
   "ankle_right": {
-    "count": 1443,
-    "mean": 0.079468,
-    "median": 0.032419,
-    "std": 0.107852,
-    "sem": 0.00284,
+    "count": 1445,
+    "mean": 0.079798,
+    "median": 0.032499,
+    "std": 0.108043,
+    "sem": 0.002843,
     "min": 0.001061,
-    "max": 0.4981,
-    "recall-0.025": 0.378082,
-    "recall-0.05": 0.686301,
-    "recall-0.1": 0.771918,
-    "recall-0.15": 0.810274,
-    "recall-0.25": 0.889041,
-    "recall-0.5": 0.988356,
+    "max": 0.492817,
+    "recall-0.025": 0.378767,
+    "recall-0.05": 0.682877,
+    "recall-0.1": 0.771233,
+    "recall-0.15": 0.810959,
+    "recall-0.25": 0.891096,
+    "recall-0.5": 0.989726,
     "num_labels": 1460
   },
   "joint_recalls": {
     "num_labels": 18990,
-    "recall-0.025": 0.48294,
-    "recall-0.05": 0.74397,
-    "recall-0.1": 0.92038,
-    "recall-0.15": 0.95656,
-    "recall-0.25": 0.97841,
-    "recall-0.5": 0.9961
+    "recall-0.025": 0.48257,
+    "recall-0.05": 0.74234,
+    "recall-0.1": 0.91954,
+    "recall-0.15": 0.95682,
+    "recall-0.25": 0.97873,
+    "recall-0.5": 0.99631
   }
 }
 {
   "total_parts": 20444,
-  "correct_parts": 19935,
-  "pcp": 0.975103
+  "correct_parts": 19937,
+  "pcp": 0.975201
 }
 ```
 
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index e69092c..c226dc4 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -319,6 +319,7 @@ def main():
     all_ids = []
     all_paths = []
     times = []
+    last_poses_3d = np.array([])
     for label in tqdm.tqdm(labels):
         images_2d = []
 
@@ -377,7 +378,7 @@ def main():
             poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
         else:
             poses3D = triangulate_poses.get_3d_pose(
-                poses_2d, label["cameras"], roomparams, joint_names_2d, minscore
+                poses_2d, label["cameras"], roomparams, joint_names_2d, last_poses_3d, minscore
             )
             poses2D = []
             for cam in label["cameras"]:
@@ -392,6 +393,7 @@ def main():
                 drop_few_limbs=(dataset_use != "mvor"),
             )
             poses3D = add_missing_joints(poses3D, joint_names_3d)
+        last_poses_3d = poses3D
 
         time_3d = time.time() - start
         print("3D time:", time_3d)
diff --git a/scripts/triangulate_poses.py b/scripts/triangulate_poses.py
index 8360e3d..7cf1a90 100644
--- a/scripts/triangulate_poses.py
+++ b/scripts/triangulate_poses.py
@@ -1,6 +1,5 @@
 import copy
 import math
-import time
 
 import cv2
 import numpy as np
@@ -66,6 +65,29 @@ def get_camera_P(cam):
 # ==================================================================================================
 
 
+def calc_pose_score(pose1, pose2, dist1, cam1, joint_names, use_joints):
+    """Calculates the score between two poses"""
+
+    # Select core joints
+    jids = [joint_names.index(j) for j in use_joints]
+    pose1 = pose1[jids]
+    pose2 = pose2[jids]
+    dist1 = dist1[jids]
+
+    mask = (pose1[:, 2] > 0.1) & (pose2[:, 2] > 0.1)
+    if np.sum(mask) < 3:
+        return 0.0
+
+    iscale = (cam1["width"] + cam1["height"]) / 2
+    scores = score_projection(pose1, pose2, dist1, mask, iscale)
+    score = np.mean(scores)
+
+    return score
+
+
+# ==================================================================================================
+
+
 def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_joints):
     """Triangulates a pair of persons and scores them based on the reprojection error"""
 
@@ -79,14 +101,38 @@ def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_j
     pose1 = pose1[jids]
     pose2 = pose2[jids]
 
-    poses_3d, score = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
+    poses_3d, score = triangulate_and_score(pose1, pose2, cam1, cam2, roomparams)
     return poses_3d, score
 
 
 # ==================================================================================================
 
 
-def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
+def score_projection(pose1, repro1, dists1, mask, iscale):
+
+    min_score = 0.1
+    error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[mask, 0:2], axis=1)
+
+    # Set errors of invisible reprojections to a high value
+    penalty = iscale
+    mask1b = (repro1[:, 2] < min_score)[mask]
+    error1[mask1b] = penalty
+
+    # Scale error by image size and distance to the camera
+    error1 = error1.clip(0, iscale / 4) / iscale
+    dscale1 = np.sqrt(np.mean(dists1[mask]) / 3.5)
+    error1 = error1 * dscale1
+
+    # Convert errors to a score
+    score1 = 1.0 / (1.0 + error1 * 10)
+
+    return score1
+
+
+# ==================================================================================================
+
+
+def triangulate_and_score(pose1, pose2, cam1, cam2, roomparams):
     """Triangulates a pair of persons and scores them based on the reprojection error"""
 
     # Mask out invisible joints
@@ -129,27 +175,16 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
     poses_3d = np.expand_dims(pose3d, axis=0)
     repro1, dists1 = utils_pose.project_poses(poses_3d, cam1, calc_dists=True)
     repro2, dists2 = utils_pose.project_poses(poses_3d, cam2, calc_dists=True)
-    error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[0, mask, 0:2], axis=1)
-    error2 = np.linalg.norm(pose2[mask, 0:2] - repro2[0, mask, 0:2], axis=1)
+    repro1, dists1 = repro1[0], dists1[0]
+    repro2, dists2 = repro2[0], dists2[0]
 
-    # Set errors of invisible reprojections to a high value
-    penalty = (cam1["width"] + cam1["height"]) / 2
-    mask1b = (repro1[0, :, 2] < min_score)[mask]
-    mask2b = (repro2[0, :, 2] < min_score)[mask]
-    error1[mask1b] = penalty
-    error2[mask2b] = penalty
-
-    # Convert errors to a score
-    # Scale by image size and distance to the camera
+    # Calculate scores for each view
     iscale = (cam1["width"] + cam1["height"]) / 2
-    error1 = error1.clip(0, iscale / 4) / iscale
-    error2 = error2.clip(0, iscale / 4) / iscale
-    dscale1 = np.sqrt(np.mean(dists1[0, mask]) / 3.5)
-    dscale2 = np.sqrt(np.mean(dists2[0, mask]) / 3.5)
-    error1 = error1 * dscale1
-    error2 = error2 * dscale2
-    error = (error1 + error2) / 2
-    scores = 1.0 / (1.0 + error * 10)
+    score1 = score_projection(pose1, repro1, dists1, mask, iscale)
+    score2 = score_projection(pose2, repro2, dists2, mask, iscale)
+
+    # Combine scores
+    scores = (score1 + score2) / 2
 
     # Drop lowest scores
     drop_k = math.floor(len(pose1) * 0.2)
@@ -283,7 +318,14 @@ def merge_group(poses_3d: np.ndarray, min_score: float):
 # ==================================================================================================
 
 
-def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95):
+def get_3d_pose(
+    poses_2d,
+    camparams,
+    roomparams,
+    joint_names_2d,
+    last_poses_3d=np.array([]),
+    min_score=0.95,
+):
     """Triangulates 3D poses from 2D poses of multiple views"""
 
     # Convert poses and camparams to numpy arrays
@@ -314,10 +356,42 @@ def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95)
         cam["P"] = get_camera_P(cam)
         camparams[i] = cam
 
+    # Project last 3D poses to 2D
+    last_poses_2d = []
+    last_poses_3d = np.asarray(last_poses_3d)
+    if last_poses_3d.size > 0:
+        for i in range(len(camparams)):
+            poses2d, dists = utils_pose.project_poses(last_poses_3d, camparams[i])
+            last_poses_2d.append((poses2d, dists))
+
+    # Check matches to old poses
+    threshold = min_score - 0.2
+    scored_pasts = {}
+    if last_poses_3d.size > 0:
+        for i in range(len(camparams)):
+            scored_pasts[i] = {}
+            poses = poses_2d[i]
+            last_poses, dists = last_poses_2d[i]
+            for j in range(len(last_poses)):
+                scored_pasts[i][j] = []
+                for k in range(len(poses)):
+                    score = calc_pose_score(
+                        poses[k],
+                        last_poses[j],
+                        dists[j],
+                        camparams[i],
+                        joint_names_2d,
+                        core_joints,
+                    )
+                    if score > threshold:
+                        scored_pasts[i][j].append(k)
+
     # Create pairs of persons
+    # Checks if the person was already matched to the last frame and if so only creates pairs with those
+    # Else it creates all possible pairs
     num_persons = [len(p) for p in poses_2d]
     all_pairs = []
-    for i in range(len(poses_2d)):
+    for i in range(len(camparams)):
         poses = poses_2d[i]
         for j in range(i + 1, len(poses_2d)):
             poses2 = poses_2d[j]
@@ -325,7 +399,16 @@ def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95)
                 for l in range(len(poses2)):
                     pid1 = sum(num_persons[:i]) + k
                     pid2 = sum(num_persons[:j]) + l
-                    all_pairs.append([(i, j, k, l), (pid1, pid2)])
+                    match = False
+                    if last_poses_3d.size > 0:
+                        for m in range(len(last_poses_3d)):
+                            if k in scored_pasts[i][m] and l in scored_pasts[j][m]:
+                                match = True
+                                all_pairs.append([(i, j, k, l), (pid1, pid2)])
+                            elif k in scored_pasts[i][m] or l in scored_pasts[j][m]:
+                                match = True
+                    if not match:
+                        all_pairs.append([(i, j, k, l), (pid1, pid2)])
 
     # Calculate pair scores
     for i in range(len(all_pairs)):
@@ -356,7 +439,7 @@ def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95)
         pose1 = poses_2d[pair[0][0]][pair[0][2]]
         pose2 = poses_2d[pair[0][1]][pair[0][3]]
 
-        pose_3d, _ = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
+        pose_3d, _ = triangulate_and_score(pose1, pose2, cam1, cam2, roomparams)
         pair.append(pose_3d)
 
     # Merge groups