--- a
+++ b/ViTPose/mmpose/apis/inference_tracking.py
@@ -0,0 +1,347 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import numpy as np
+
+from mmpose.core import OneEuroFilter, oks_iou
+
+
+def _compute_iou(bboxA, bboxB):
+    """Compute the Intersection over Union (IoU) between two boxes .
+
+    Args:
+        bboxA (list): The first bbox info (left, top, right, bottom, score).
+        bboxB (list): The second bbox info (left, top, right, bottom, score).
+
+    Returns:
+        float: The IoU value.
+    """
+
+    x1 = max(bboxA[0], bboxB[0])
+    y1 = max(bboxA[1], bboxB[1])
+    x2 = min(bboxA[2], bboxB[2])
+    y2 = min(bboxA[3], bboxB[3])
+
+    inter_area = max(0, x2 - x1) * max(0, y2 - y1)
+
+    bboxA_area = (bboxA[2] - bboxA[0]) * (bboxA[3] - bboxA[1])
+    bboxB_area = (bboxB[2] - bboxB[0]) * (bboxB[3] - bboxB[1])
+    union_area = float(bboxA_area + bboxB_area - inter_area)
+    if union_area == 0:
+        union_area = 1e-5
+        warnings.warn('union_area=0 is unexpected')
+
+    iou = inter_area / union_area
+
+    return iou
+
+
+def _track_by_iou(res, results_last, thr):
+    """Get track id using IoU tracking greedily.
+
+    Args:
+        res (dict): The bbox & pose results of the person instance.
+        results_last (list[dict]): The bbox & pose & track_id info of the
+            last frame (bbox_result, pose_result, track_id).
+        thr (float): The threshold for iou tracking.
+
+    Returns:
+        int: The track id for the new person instance.
+        list[dict]: The bbox & pose & track_id info of the persons
+            that have not been matched on the last frame.
+        dict: The matched person instance on the last frame.
+    """
+
+    bbox = list(res['bbox'])
+
+    max_iou_score = -1
+    max_index = -1
+    match_result = {}
+    for index, res_last in enumerate(results_last):
+        bbox_last = list(res_last['bbox'])
+
+        iou_score = _compute_iou(bbox, bbox_last)
+        if iou_score > max_iou_score:
+            max_iou_score = iou_score
+            max_index = index
+
+    if max_iou_score > thr:
+        track_id = results_last[max_index]['track_id']
+        match_result = results_last[max_index]
+        del results_last[max_index]
+    else:
+        track_id = -1
+
+    return track_id, results_last, match_result
+
+
+def _track_by_oks(res, results_last, thr):
+    """Get track id using OKS tracking greedily.
+
+    Args:
+        res (dict): The pose results of the person instance.
+        results_last (list[dict]): The pose & track_id info of the
+            last frame (pose_result, track_id).
+        thr (float): The threshold for oks tracking.
+
+    Returns:
+        int: The track id for the new person instance.
+        list[dict]: The pose & track_id info of the persons
+            that have not been matched on the last frame.
+        dict: The matched person instance on the last frame.
+    """
+    pose = res['keypoints'].reshape((-1))
+    area = res['area']
+    max_index = -1
+    match_result = {}
+
+    if len(results_last) == 0:
+        return -1, results_last, match_result
+
+    pose_last = np.array(
+        [res_last['keypoints'].reshape((-1)) for res_last in results_last])
+    area_last = np.array([res_last['area'] for res_last in results_last])
+
+    oks_score = oks_iou(pose, pose_last, area, area_last)
+
+    max_index = np.argmax(oks_score)
+
+    if oks_score[max_index] > thr:
+        track_id = results_last[max_index]['track_id']
+        match_result = results_last[max_index]
+        del results_last[max_index]
+    else:
+        track_id = -1
+
+    return track_id, results_last, match_result
+
+
+def _get_area(results):
+    """Get bbox for each person instance on the current frame.
+
+    Args:
+        results (list[dict]): The pose results of the current frame
+            (pose_result).
+    Returns:
+        list[dict]: The bbox & pose info of the current frame
+            (bbox_result, pose_result, area).
+    """
+    for result in results:
+        if 'bbox' in result:
+            result['area'] = ((result['bbox'][2] - result['bbox'][0]) *
+                              (result['bbox'][3] - result['bbox'][1]))
+        else:
+            xmin = np.min(
+                result['keypoints'][:, 0][result['keypoints'][:, 0] > 0],
+                initial=1e10)
+            xmax = np.max(result['keypoints'][:, 0])
+            ymin = np.min(
+                result['keypoints'][:, 1][result['keypoints'][:, 1] > 0],
+                initial=1e10)
+            ymax = np.max(result['keypoints'][:, 1])
+            result['area'] = (xmax - xmin) * (ymax - ymin)
+            result['bbox'] = np.array([xmin, ymin, xmax, ymax])
+    return results
+
+
+def _temporal_refine(result, match_result, fps=None):
+    """Refine koypoints using tracked person instance on last frame.
+
+    Args:
+        results (dict): The pose results of the current frame
+                (pose_result).
+        match_result (dict): The pose results of the last frame
+                (match_result)
+    Returns:
+        (array): The person keypoints after refine.
+    """
+    if 'one_euro' in match_result:
+        result['keypoints'][:, :2] = match_result['one_euro'](
+            result['keypoints'][:, :2])
+        result['one_euro'] = match_result['one_euro']
+    else:
+        result['one_euro'] = OneEuroFilter(result['keypoints'][:, :2], fps=fps)
+    return result['keypoints']
+
+
+def get_track_id(results,
+                 results_last,
+                 next_id,
+                 min_keypoints=3,
+                 use_oks=False,
+                 tracking_thr=0.3,
+                 use_one_euro=False,
+                 fps=None):
+    """Get track id for each person instance on the current frame.
+
+    Args:
+        results (list[dict]): The bbox & pose results of the current frame
+            (bbox_result, pose_result).
+        results_last (list[dict]): The bbox & pose & track_id info of the
+            last frame (bbox_result, pose_result, track_id).
+        next_id (int): The track id for the new person instance.
+        min_keypoints (int): Minimum number of keypoints recognized as person.
+            default: 3.
+        use_oks (bool): Flag to using oks tracking. default: False.
+        tracking_thr (float): The threshold for tracking.
+        use_one_euro (bool): Option to use one-euro-filter. default: False.
+        fps (optional): Parameters that d_cutoff
+            when one-euro-filter is used as a video input
+
+    Returns:
+        tuple:
+        - results (list[dict]): The bbox & pose & track_id info of the \
+            current frame (bbox_result, pose_result, track_id).
+        - next_id (int): The track id for the new person instance.
+    """
+    results = _get_area(results)
+
+    if use_oks:
+        _track = _track_by_oks
+    else:
+        _track = _track_by_iou
+
+    for result in results:
+        track_id, results_last, match_result = _track(result, results_last,
+                                                      tracking_thr)
+        if track_id == -1:
+            if np.count_nonzero(result['keypoints'][:, 1]) > min_keypoints:
+                result['track_id'] = next_id
+                next_id += 1
+            else:
+                # If the number of keypoints detected is small,
+                # delete that person instance.
+                result['keypoints'][:, 1] = -10
+                result['bbox'] *= 0
+                result['track_id'] = -1
+        else:
+            result['track_id'] = track_id
+        if use_one_euro:
+            result['keypoints'] = _temporal_refine(
+                result, match_result, fps=fps)
+        del match_result
+
+    return results, next_id
+
+
+def vis_pose_tracking_result(model,
+                             img,
+                             result,
+                             radius=4,
+                             thickness=1,
+                             kpt_score_thr=0.3,
+                             dataset='TopDownCocoDataset',
+                             dataset_info=None,
+                             show=False,
+                             out_file=None):
+    """Visualize the pose tracking results on the image.
+
+    Args:
+        model (nn.Module): The loaded detector.
+        img (str | np.ndarray): Image filename or loaded image.
+        result (list[dict]): The results to draw over `img`
+            (bbox_result, pose_result).
+        radius (int): Radius of circles.
+        thickness (int): Thickness of lines.
+        kpt_score_thr (float): The threshold to visualize the keypoints.
+        skeleton (list[tuple]): Default None.
+        show (bool):  Whether to show the image. Default True.
+        out_file (str|None): The filename of the output visualization image.
+    """
+    if hasattr(model, 'module'):
+        model = model.module
+
+    palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],
+                        [230, 230, 0], [255, 153, 255], [153, 204, 255],
+                        [255, 102, 255], [255, 51, 255], [102, 178, 255],
+                        [51, 153, 255], [255, 153, 153], [255, 102, 102],
+                        [255, 51, 51], [153, 255, 153], [102, 255, 102],
+                        [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],
+                        [255, 255, 255]])
+
+    if dataset_info is None and dataset is not None:
+        warnings.warn(
+            'dataset is deprecated.'
+            'Please set `dataset_info` in the config.'
+            'Check https://github.com/open-mmlab/mmpose/pull/663 for details.',
+            DeprecationWarning)
+        # TODO: These will be removed in the later versions.
+        if dataset in ('TopDownCocoDataset', 'BottomUpCocoDataset',
+                       'TopDownOCHumanDataset'):
+            kpt_num = 17
+            skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],
+                        [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],
+                        [8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4],
+                        [3, 5], [4, 6]]
+
+        elif dataset == 'TopDownCocoWholeBodyDataset':
+            kpt_num = 133
+            skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],
+                        [5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],
+                        [8, 10], [1, 2], [0, 1], [0, 2],
+                        [1, 3], [2, 4], [3, 5], [4, 6], [15, 17], [15, 18],
+                        [15, 19], [16, 20], [16, 21], [16, 22], [91, 92],
+                        [92, 93], [93, 94], [94, 95], [91, 96], [96, 97],
+                        [97, 98], [98, 99], [91, 100], [100, 101], [101, 102],
+                        [102, 103], [91, 104], [104, 105], [105, 106],
+                        [106, 107], [91, 108], [108, 109], [109, 110],
+                        [110, 111], [112, 113], [113, 114], [114, 115],
+                        [115, 116], [112, 117], [117, 118], [118, 119],
+                        [119, 120], [112, 121], [121, 122], [122, 123],
+                        [123, 124], [112, 125], [125, 126], [126, 127],
+                        [127, 128], [112, 129], [129, 130], [130, 131],
+                        [131, 132]]
+            radius = 1
+
+        elif dataset == 'TopDownAicDataset':
+            kpt_num = 14
+            skeleton = [[2, 1], [1, 0], [0, 13], [13, 3], [3, 4], [4, 5],
+                        [8, 7], [7, 6], [6, 9], [9, 10], [10, 11], [12, 13],
+                        [0, 6], [3, 9]]
+
+        elif dataset == 'TopDownMpiiDataset':
+            kpt_num = 16
+            skeleton = [[0, 1], [1, 2], [2, 6], [6, 3], [3, 4], [4, 5], [6, 7],
+                        [7, 8], [8, 9], [8, 12], [12, 11], [11, 10], [8, 13],
+                        [13, 14], [14, 15]]
+
+        elif dataset in ('OneHand10KDataset', 'FreiHandDataset',
+                         'PanopticDataset'):
+            kpt_num = 21
+            skeleton = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7],
+                        [7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13],
+                        [13, 14], [14, 15], [15, 16], [0, 17], [17, 18],
+                        [18, 19], [19, 20]]
+
+        elif dataset == 'InterHand2DDataset':
+            kpt_num = 21
+            skeleton = [[0, 1], [1, 2], [2, 3], [4, 5], [5, 6], [6, 7], [8, 9],
+                        [9, 10], [10, 11], [12, 13], [13, 14], [14, 15],
+                        [16, 17], [17, 18], [18, 19], [3, 20], [7, 20],
+                        [11, 20], [15, 20], [19, 20]]
+
+        else:
+            raise NotImplementedError()
+
+    elif dataset_info is not None:
+        kpt_num = dataset_info.keypoint_num
+        skeleton = dataset_info.skeleton
+
+    for res in result:
+        track_id = res['track_id']
+        bbox_color = palette[track_id % len(palette)]
+        pose_kpt_color = palette[[track_id % len(palette)] * kpt_num]
+        pose_link_color = palette[[track_id % len(palette)] * len(skeleton)]
+        img = model.show_result(
+            img, [res],
+            skeleton,
+            radius=radius,
+            thickness=thickness,
+            pose_kpt_color=pose_kpt_color,
+            pose_link_color=pose_link_color,
+            bbox_color=tuple(bbox_color.tolist()),
+            kpt_score_thr=kpt_score_thr,
+            show=show,
+            out_file=out_file)
+
+    return img