diff --git a/Pose2Sim/calibration.py b/Pose2Sim/calibration.py
index b0d8e0f6..5d41d3a1 100644
--- a/Pose2Sim/calibration.py
+++ b/Pose2Sim/calibration.py
@@ -759,12 +759,12 @@ def calibrate_intrinsics(calib_dir, intrinsics_config_dict, save_debug_images=Tr
             if show_detection_intrinsics == True:
                 # If previously labeled points exist, check if they are satisfying
                 if 'image_points' in locals():
-                    imgp = next((entry['image_points_2d'] for entry in image_points if entry["cam_name"] == cam_name), [])
-                    objp = next((entry['object_points_3d'] for entry in image_points if entry["cam_name"] == cam_name), [])
-                    if len(imgp) > 0 and len(objp) > 0:
+                    cached_imgp = next((entry['image_points_2d'] for entry in image_points if entry["cam_name"] == cam_name), [])
+                    cached_objp = next((entry['object_points_3d'] for entry in image_points if entry["cam_name"] == cam_name), [])
+                    if len(cached_imgp) > 0 and len(cached_objp) > 0:
                         # recalculate reprojected points
-                        imgp = np.array(imgp).reshape(-1, 2)
-                        objp = np.array(objp).reshape(-1, 3)
+                        imgp = np.array(cached_imgp).reshape(-1, 2)
+                        objp = np.array(cached_objp).reshape(-1, 3)
                         saved_img_path = create_image_labels(img_path, imgp, calib_dir, 'int', reprojected_points=None, show=True, save=save_debug_images)
                         # Are you satisfied? If so, add to imgpoints and continue; else, redo detection
                         satisfied = show_qt_message_box(
@@ -1440,7 +1440,7 @@ def extract_frames(video_path, extract_every_N_sec=1, overwrite_extraction=False
     - extracted frames in folder
     '''
     
-    if not Path(Path(video_path).exists().stem + '_00000.png') or overwrite_extraction:
+    if not list(Path(video_path).parent.glob(f'{Path(video_path).stem}_*.png')) or overwrite_extraction:
         cap = cv2.VideoCapture(str(video_path))
         if cap.isOpened():
             fps = round(cap.get(cv2.CAP_PROP_FPS))
@@ -1450,7 +1450,7 @@ def extract_frames(video_path, extract_every_N_sec=1, overwrite_extraction=False
                 ret, frame = cap.read()
                 if ret == True:
                     if frame_nb % (fps*extract_every_N_sec) == 0:
-                        img_path = (Path(video_path).stem + '_' +str(frame_nb).zfill(5)+'.png')
+                        img_path = Path(video_path).parent / (Path(video_path).stem + '_' +str(frame_nb).zfill(5)+'.png')
                         cv2.imwrite(str(img_path), frame)
                     frame_nb+=1
                 else:
diff --git a/Pose2Sim/markerAugmentation.py b/Pose2Sim/markerAugmentation.py
index 5babaf2a..d1af3f37 100644
--- a/Pose2Sim/markerAugmentation.py
+++ b/Pose2Sim/markerAugmentation.py
@@ -89,7 +89,8 @@ def augment_markers_all(config_dict):
     frame_range = config_dict.get('project', {}).get('frame_range', 'auto')
     subject_height = config_dict.get('project', {}).get('participant_height', 'auto')
     subject_mass = config_dict.get('project', {}).get('participant_mass', 70.0)
-    
+    multi_person = config_dict.get('project', {}).get('multi_person', False)
+
     large_hip_knee_angles = config_dict.get('kinematics', {}).get('large_hip_knee_angles', 90)
     trimmed_extrema_percent = config_dict.get('kinematics', {}).get('trimmed_extrema_percent', 50)
     default_height = config_dict.get('kinematics', {}).get('default_height', 1.7)
@@ -112,6 +113,12 @@ def augment_markers_all(config_dict):
         trc_files = trc_no_filtering
     sorted(trc_files, key=natural_sort_key)
 
+    if not multi_person and len(trc_files) > 1:
+        most_recent_trc_file = max(trc_files, key=lambda f: f.stat().st_mtime)
+        ignored_trc_files = [f.name for f in trc_files if f != most_recent_trc_file]
+        logging.warning(f"multi_person is set to false in Config.toml but {len(trc_files)} trc files were found in {pose_3d_dir}. Using the most recently modified one ({most_recent_trc_file.name}) and ignoring {ignored_trc_files}.")
+        trc_files = [most_recent_trc_file]
+
     # Add missing markers if needed
     for trc_file in trc_files:
         # Import TRC file
@@ -160,9 +167,15 @@ def augment_markers_all(config_dict):
             subject_height.append(height)
     elif not type(subject_height) == list: # int or float
         subject_height = [subject_height]
-    if len(subject_height) < len(trc_files):
-        logging.warning(f"Number of subject heights does not match number of TRC files. Missing heights are set to {default_height}m.")
-        subject_height += [default_height] * (len(trc_files) - len(subject_height))
+    if len(subject_height) != len(trc_files):
+        if not multi_person:
+            raise ValueError(f"Multi_person is set to false in Config.toml but participant_height has {len(subject_height)} value(s) while {len(trc_files)} trc file(s) were found. Expected exactly one matching value.")
+        elif len(subject_height) < len(trc_files):
+            logging.warning(f"Number of subject heights does not match number of TRC files. Missing heights are set to {default_height}m.")
+            subject_height += [default_height] * (len(trc_files) - len(subject_height))
+        else:
+            logging.warning("Number of subject heights does not match number of TRC files. Extra heights are ignored.")
+            subject_height = subject_height[:len(trc_files)]
 
     # Get subject masses
     if subject_mass is None or subject_mass == 0:
@@ -170,12 +183,18 @@ def augment_markers_all(config_dict):
         logging.warning("No subject mass found in Config.toml. Using default mass of 70kg.")
     elif not type(subject_mass) == list:
         subject_mass = [subject_mass]
-    if len(subject_mass) < len(trc_files):
-        logging.warning("Number of subject masses does not match number of TRC files. Missing masses are set to 70kg.")
-        subject_mass += [70] * (len(trc_files) - len(subject_mass))
+    if len(subject_mass) != len(trc_files):
+        if not multi_person:
+            raise ValueError(f"Multi_person is set to false in Config.toml but participant_mass has {len(subject_mass)} value(s) while {len(trc_files)} trc file(s) were found. Expected exactly one matching value.")
+        elif len(subject_mass) < len(trc_files):
+            logging.warning("Number of subject masses does not match number of TRC files. Missing masses are set to 70kg.")
+            subject_mass += [70] * (len(trc_files) - len(subject_mass))
+        else:
+            logging.warning("Number of subject masses does not match number of TRC files. Extra masses are ignored.")
+            subject_mass = subject_mass[:len(trc_files)]
 
     # Run marker augmentation
-    for p in range(len(subject_mass)):
+    for p in range(len(trc_files)):
         trc_file = trc_files[p]
         trc_file_out = Path(trc_file).stem + f'_{augmenterModelName}.trc'
         
diff --git a/Pose2Sim/synchronization.py b/Pose2Sim/synchronization.py
index fbf62528..2b371a61 100644
--- a/Pose2Sim/synchronization.py
+++ b/Pose2Sim/synchronization.py
@@ -1490,7 +1490,7 @@ def synchronize_cams_all(config_dict):
     nb_frames_per_cam = [len(fnmatch.filter(os.listdir(json_dir), '*.json')) for json_dir in json_dirs]
     cam_nb = len(json_dirs)
     cam_list = list(range(cam_nb))
-    cam_names = [Path(j_dir).name.split('_')[0] for j_dir in json_dirs]
+    cam_names = [re.sub(r'_json$', '', Path(j_dir).name) for j_dir in json_dirs]
     
     # frame range selection
     f_range = [[0, min([len(j) for j in json_files_names])] if frame_range in ('all', 'auto', []) else frame_range][0]