diff --git a/MOT_labeling/annotations/train.json b/MOT_labeling/annotations/train.json
new file mode 100644
index 0000000..049793f
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diff --git a/lego_labeling_samples/carema_0002/gt/autosave_label.txt b/lego_labeling_samples/carema_0002/gt/autosave_label.txt
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+++ b/lego_labeling_samples/carema_0002/gt/autosave_label.txt
@@ -0,0 +1,2 @@
+1 space_mini_figure 67 159 36 
+2 town_mini_figure 204 43 41 
diff --git a/lego_labeling_samples/carema_0002/gt/coco.json b/lego_labeling_samples/carema_0002/gt/coco.json
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+++ b/lego_labeling_samples/carema_0002/gt/coco.json
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+{"categories": [{"id": 1, "name": "space_mini_figure", "color": [67, 159, 36], "supercategory": ""}, {"id": 2, "name": "town_mini_figure", "color": [204, 43, 41], "supercategory": ""}],
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+  "annotations": [
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\ No newline at end of file
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diff --git a/tools/eiseg2coco_MOT.py b/tools/eiseg2coco_MOT.py
new file mode 100644
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+++ b/tools/eiseg2coco_MOT.py
@@ -0,0 +1,213 @@
+import argparse
+import glob
+import json
+import os
+import shutil
+# follow openMMDet path processing patterns
+from pathlib import Path
+import numpy as np
+import PIL.ImageDraw
+
+from sklearn.model_selection import train_test_split
+
+from mtmc.config import settings
+
+useQtBottomLeft = True  # should be Qt TopLeft
+
+"""
+Add MOT configuration, with following fields:
+<carema_id> <frame_id><conf><ignore> <visibility/ratio>
+By now the given data has no occlusion issues. And all labeling are visible.
+Considering that we may need inference result of detection, set conf=1 to match inference
+set ignore = 0 and visibility_ratio = 1 to use all annotations
+carema_id and frame_id are extracted automatically from folder and filename
+
+In MTMC we may need to consider how multiple caremas associate together.
+So we split dataset after merging all annotations and image.
+This requires a refactor of the current annotation structures.
+"""
+
+class MyEncoder(json.JSONEncoder):
+    def default(self, val):
+        if isinstance(val, np.integer):
+            return int(val)
+        elif isinstance(val, np.floating):
+            return float(val)
+        elif isinstance(val, np.ndarray):
+            return val.tolist()
+        else:
+            return super(MyEncoder, self).default(val)
+
+
+def merge_coco_annotation(coco_annotation_paths):
+    data_coco = {
+        "images": [],
+        "annotations": []
+    }
+    coco_image_id = 0
+    imgs_info_indexed_by_img_name = {}
+    imgs_info_indexed_by_img_idx = {}
+
+    # print('Generating dataset from:', coco_annotation_path)
+    for coco_annotation_path in coco_annotation_paths:
+        with open(coco_annotation_path) as f:
+            data = json.load(f)
+            # fetch categroies, info and licenses fields
+            # data_coco["categories"] = data["categories"]
+            # data_coco["info"] = data["info"]
+            # data_coco["licenses"] = data["licenses"]
+            # fetch images and annotations
+        camera_id = coco_annotation_path.split("/")[-3]
+        for idx, img_info in enumerate(data['images']):
+            # For image, make sure you update id and filename
+            img_name = img_info["file_name"]
+            img_info["old_id"] = img_info["id"]
+            img_name = camera_id + "_" + img_name
+            img_info["file_name"] = img_name
+            img_info["id"] = coco_image_id
+            if imgs_info_indexed_by_img_name.get(img_name, None) is None:
+                imgs_info_indexed_by_img_name[img_name] = {}
+            imgs_info_indexed_by_img_name[img_name]["img_info"] = img_info
+            imgs_info_indexed_by_img_idx[img_info["id"]] = img_info
+            for record in data["annotations"]:
+                if "img_id_update" not in record and record["image_id"] == img_info["old_id"]:
+                    record["image_id"] = img_info["id"]
+                    record["img_id_update"] = True
+            coco_image_id += 1
+
+        for annotation in data['annotations']:
+            # if you update image_id, then reflect in annotations
+            # update image_id and file_name and camera_id
+            # rest is fine. Do whatever you want
+            # need post visualization on this
+            img_idx = annotation["image_id"]
+            img_info = imgs_info_indexed_by_img_idx[img_idx]
+            img_name = img_info["file_name"]
+            print("img:",img_name)
+            # annotation polygon
+            epsilon = 1e-5
+            if 'iscrowd' not in annotation:
+                annotation['iscrowd'] = 0
+            if annotation['area'] < epsilon:
+                annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
+            if useQtBottomLeft:
+                annotation['bbox'][1] = annotation['bbox'][1] - annotation['bbox'][3]
+
+            annotation["carema_id"] = camera_id
+            annotation["frame_id"] = img_name.split("_")[-1].split(".")[0]
+            annotation["conf"] = 1
+            annotation["ignore"] = 0
+            annotation["visibility_ratio"] = 1
+            imgs_info_indexed_by_img_name[img_name]["img_annotation"] = annotation
+    return imgs_info_indexed_by_img_name
+
+def save_coco_annotations(dataset, imgs_info_indexed_by_img_name):
+    data_coco = {
+        "images": [],
+        "annotations": []
+    }
+    for img_path in dataset:
+        img_file = Path(img_path)
+        img_name = img_path.split("/")[-3] + "_" + img_file.name
+        img_info_with_annotation = imgs_info_indexed_by_img_name[img_name]
+        print(img_name)
+        data_coco["images"].append(img_info_with_annotation["img_info"])
+        data_coco["annotations"].append(img_info_with_annotation["img_annotation"])
+    return data_coco
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--dataset_type', default='eiseg', help='type of dataset is supported is EISeg')
+    parser.add_argument('--json_input_dir', default='%s/data/%s' % (settings.PROJECT_ROOT, settings.TEST_DATASET),
+                        help='input annotation directory')
+    parser.add_argument('--image_input_dir', default='%s/data/%s' % (settings.PROJECT_ROOT, settings.TEST_DATASET),
+                        help='image directory')
+    parser.add_argument('--output_dir',
+                        default='%s/data/MOT_labeling' % (settings.PROJECT_ROOT),
+                        help='output dataset directory')
+
+    args = parser.parse_args()
+    try:
+        assert args.dataset_type in ['coco', 'eiseg', 'labelme']
+    except AssertionError as e:
+        print('Only coco and EISeg dataset is supported for the moment!')
+        return -1
+
+    coco_annotation_dir = args.json_input_dir
+    camera_available = []
+    for root, dirs, files in os.walk(coco_annotation_dir, topdown=True):
+        camera_available.append(dirs)
+        break
+    camera_available = camera_available[0]
+
+
+    assert (settings.TRAIN_PROPORTION > 0 and settings.TRAIN_PROPORTION < 1)
+    assert (settings.TRAIN_PROPORTION + settings.VALIDATION_PROPORTION == 1)
+
+    setattr(settings, 'coco_dataset_path', args.output_dir)
+    train_path = os.path.join(settings.coco_dataset_path, "train")
+    val_path = os.path.join(settings.coco_dataset_path, "val")
+    annotation_path = os.path.join(settings.coco_dataset_path, "annotations")
+    if not os.path.exists(settings.coco_dataset_path):
+        os.makedirs(train_path)
+        os.makedirs(val_path)
+        os.makedirs(annotation_path)
+
+    setattr(settings, 'DATA_DIR', args.image_input_dir)
+    comb_images = []
+    for camera in camera_available:
+        raw_images = list(glob.iglob(os.path.join(settings.DATA_DIR, camera, "img", "*.jpg")))
+        raw_images = sorted(raw_images, key=lambda x: int(os.path.split(x)[1].split('.')[0]))
+        comb_images += raw_images
+
+    train_dataset, val_dataset = train_test_split(comb_images, train_size=settings.TRAIN_PROPORTION)
+    # Disk task : move images and annotation files to coco files hierarchy
+    # TODO: new_name: add carema id to current filename update image_id and filename accordingly
+    for img_path in train_dataset:
+        img_name = Path(img_path).name  # similar to boot api
+        img_name_to = img_path.split("/")[-3] + "_" + img_name
+        shutil.copyfile(
+            img_path,
+            os.path.join(train_path, img_name_to)
+        )
+
+    for img_path in val_dataset:
+        img_name = Path(img_path).name  # similar to boot api
+        img_name_to = img_path.split("/")[-3] + "_" + img_name
+        shutil.copyfile(
+            img_path,
+            os.path.join(val_path, img_name_to)
+        )
+
+    coco_annotation_files = []
+    for camera in camera_available:
+        # carema -> carema_0001
+        coco_annotation_file = os.path.join(coco_annotation_dir, camera, "gt", "coco.json")
+        coco_annotation_files.append(coco_annotation_file)
+
+    dataset = merge_coco_annotation(coco_annotation_files)
+    train_coco_col = save_coco_annotations(train_dataset, dataset)
+    val_coco_col = save_coco_annotations(val_dataset, dataset)
+
+    train_json_path = os.path.join(annotation_path, "train.json")
+    json.dump(
+        train_coco_col,
+        open(train_json_path, 'w'),
+        indent=4,
+        cls=MyEncoder
+    )
+
+    val_json_path = os.path.join(annotation_path, "val.json")
+    json.dump(
+        val_coco_col,
+        open(val_json_path, 'w'),
+        indent=4,
+        cls=MyEncoder
+    )
+    return 0
+
+if __name__ == '__main__':
+    import sys
+    sys.exit(main())