[Feature] Support OC-SORT for MOT

configs/mot/ocsort/README.md

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+# Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Multi-Object Tracking (MOT) has rapidly progressed with the development of object detection and re-identification. However, motion modeling, which facilitates object association by forecasting short-term trajec- tories with past observations, has been relatively under-explored in recent years. Current motion models in MOT typically assume that the object motion is linear in a small time window and needs continuous observations, so these methods are sensitive to occlusions and non-linear motion and require high frame-rate videos. In this work, we show that a simple motion model can obtain state-of-the-art tracking performance without other cues like appearance. We emphasize the role of “observation” when recovering tracks from being lost and reducing the error accumulated by linear motion models during the lost period. We thus name the proposed method as Observation-Centric SORT, OC-SORT for short. It remains simple, online, and real-time but improves robustness over occlusion and non-linear motion. It achieves 63.2 and 62.1 HOTA on MOT17 and MOT20, respectively, surpassing all published methods. It also sets new states of the art on KITTI Pedestrian Tracking and DanceTrack where the object motion is highly non-linear
+
+<!-- [IMAGE] -->
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/17743251/168193097-b3ad1a94-b18c-4b14-b7b1-5f8c6ed842f0.png"/>
+</div>
+
+## Citation
+
+<!-- [ALGORITHM] -->
+
+```latex
+@article{cao2022observation,
+  title={Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking},
+  author={Cao, Jinkun and Weng, Xinshuo and Khirodkar, Rawal and Pang, Jiangmiao and Kitani, Kris},
+  journal={arXiv preprint arXiv:2203.14360},
+  year={2022}
+}
+```
+
+## Results and models on MOT17
+
+The performance on `MOT17-half-val` is comparable with the performance from [the OC-SORT official implementation](https://github.com/noahcao/OC_SORT). We use the same YOLO-X detector weights as in [ByteTrack](https://github.com/open-mmlab/mmtracking/tree/master/configs/mot/bytetrack).
+
+| Method  | Detector |        Train Set        | Test Set | Public | Inf time (fps) | HOTA  | MOTA  | IDF1  |  FP   |  FN   | IDSw. |                          Config                           |                                                                                  Download                                                                                  |
+| :-----: | :------: | :---------------------: | :------: | :----: | :------------: | :---: | :---: | :---: | :---: | :---: | :---: | :-------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| OC-SORT | YOLOX-X  | CrowdHuman + half-train | half-val |   N    |       -        | 67.7  | 76.4  | 77.5  | 18516 | 19014 |  909  | [config](ocsort_yolox_x_crowdhuman_mot17-private-half.py) | [model](https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth) &#124; log |

configs/mot/ocsort/metafile.yml

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+Collections:
+  - Name: OCSORT
+    Metadata:
+      Training Techniques:
+        - SGD with Momentum
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - YOLOX
+    Paper:
+        URL: https://arxiv.org/abs/2203.14360
+        Title: Observation-Centric SORT Rethinking SORT for Robust Multi-Object Tracking
+    README: configs/mot/ocsort/README.md
+
+Models:
+  - Name: ocsort_yolox_x_crowdhuman_mot17-private-half
+    In Collection: OCSORT
+    Config: configs/mot/ocsort/ocsort_yolox_x_crowdhuman_mot17-private-half.py
+    Metadata:
+      Training Data: CrowdHuman + MOT17-half-train
+    Results:
+      - Task: Multiple Object Tracking
+        Dataset: MOT17-half-val
+        Metrics:
+          MOTA: 78.6
+          IDF1: 79.2
+    Weights: https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth
+
+  - Name: ocsort_yolox_x_crowdhuman_mot17-private
+    In Collection: OCSORT
+    Config: configs/mot/ocsort/ocsort_yolox_x_crowdhuman_mot17-private.py
+    Metadata:
+      Training Data: CrowdHuman + MOT17-half-train
+    Results:
+      - Task: Multiple Object Tracking
+        Dataset: MOT17-test
+        Metrics:
+          MOTA: 78.1
+          IDF1: 74.8
+    Weights: https://download.openmmlab.com/mmtracking/mot/bytetrack/bytetrack_yolox_x/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth

configs/mot/ocsort/ocsort_yolox_x_crowdhuman_mot17-private-half.py

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+_base_ = [
+    '../../_base_/models/yolox_x_8x8.py',
+    '../../_base_/datasets/mot_challenge.py', '../../_base_/default_runtime.py'
+]
+
+img_scale = (800, 1440)
+samples_per_gpu = 4
+
+model = dict(
+    type='OCSORT',
+    detector=dict(
+        input_size=img_scale,
+        random_size_range=(18, 32),
+        bbox_head=dict(num_classes=1),
+        test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.7)),
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=  # noqa: E251
+            'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth'  # noqa: E501
+        )),
+    motion=dict(type='KalmanFilter'),
+    tracker=dict(
+        type='OCSORTTracker',
+        obj_score_thr=0.3,
+        init_track_thr=0.7,
+        weight_iou_with_det_scores=True,
+        match_iou_thr=0.3,
+        num_tentatives=3,
+        vel_consist_weight=0.2,
+        vel_delta_t=3,
+        num_frames_retain=30))
+
+train_pipeline = [
+    dict(
+        type='Mosaic',
+        img_scale=img_scale,
+        pad_val=114.0,
+        bbox_clip_border=False),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2),
+        bbox_clip_border=False),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0,
+        bbox_clip_border=False),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='Resize',
+        img_scale=img_scale,
+        keep_ratio=True,
+        bbox_clip_border=False),
+    dict(type='Pad', size_divisor=32, pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[0.0, 0.0, 0.0],
+                std=[1.0, 1.0, 1.0],
+                to_rgb=False),
+            dict(
+                type='Pad',
+                size_divisor=32,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='VideoCollect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=samples_per_gpu,
+    workers_per_gpu=4,
+    persistent_workers=True,
+    train=dict(
+        _delete_=True,
+        type='MultiImageMixDataset',
+        dataset=dict(
+            type='CocoDataset',
+            ann_file=[
+                'data/MOT17/annotations/half-train_cocoformat.json',
+                'data/crowdhuman/annotations/crowdhuman_train.json',
+                'data/crowdhuman/annotations/crowdhuman_val.json'
+            ],
+            img_prefix=[
+                'data/MOT17/train', 'data/crowdhuman/train',
+                'data/crowdhuman/val'
+            ],
+            classes=('pedestrian', ),
+            pipeline=[
+                dict(type='LoadImageFromFile'),
+                dict(type='LoadAnnotations', with_bbox=True)
+            ],
+            filter_empty_gt=False),
+        pipeline=train_pipeline),
+    val=dict(
+        pipeline=test_pipeline,
+        interpolate_tracks_cfg=dict(min_num_frames=5, max_num_frames=20)),
+    test=dict(
+        pipeline=test_pipeline,
+        interpolate_tracks_cfg=dict(min_num_frames=5, max_num_frames=20)))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.001 / 8 * samples_per_gpu,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
+optimizer_config = dict(grad_clip=None)
+
+# some hyper parameters
+total_epochs = 80
+num_last_epochs = 10
+resume_from = None
+interval = 5
+
+# learning policy
+lr_config = dict(
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.05)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+
+checkpoint_config = dict(interval=1)
+evaluation = dict(metric=['bbox', 'track'], interval=1)
+search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML']
+
+# you need to set mode='dynamic' if you are using pytorch<=1.5.0
+fp16 = dict(loss_scale=dict(init_scale=512.))

configs/mot/ocsort/ocsort_yolox_x_crowdhuman_mot17-private.py

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+_base_ = ['./ocsort_yolox_x_crowdhuman_mot17-private-half.py']
+
+data = dict(
+    test=dict(
+        ann_file='data/MOT17/annotations/test_cocoformat.json',
+        img_prefix='data/MOT17/test'))

configs/mot/ocsort/ocsort_yolox_x_crowdhuman_mot20-private-half.py

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+_base_ = ['./ocsort_yolox_x_crowdhuman_mot17-private-half.py']
+
+img_scale = (896, 1600)
+
+model = dict(
+    detector=dict(input_size=img_scale, random_size_range=(20, 36)),
+    tracker=dict(
+        weight_iou_with_det_scores=False,
+        match_iou_thr=0.3,
+    ))
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(type='Pad', size_divisor=32, pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Normalize',
+                mean=[0.0, 0.0, 0.0],
+                std=[1.0, 1.0, 1.0],
+                to_rgb=False),
+            dict(
+                type='Pad',
+                size_divisor=32,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='VideoCollect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(
+        dataset=dict(
+            ann_file=[
+                'data/MOT20/annotations/half-train_cocoformat.json',
+                'data/crowdhuman/annotations/crowdhuman_train.json',
+                'data/crowdhuman/annotations/crowdhuman_val.json'
+            ],
+            img_prefix=[
+                'data/MOT20/train', 'data/crowdhuman/train',
+                'data/crowdhuman/val'
+            ]),
+        pipeline=train_pipeline),
+    val=dict(
+        ann_file='data/MOT20/annotations/half-val_cocoformat.json',
+        img_prefix='data/MOT20/train',
+        pipeline=test_pipeline),
+    test=dict(
+        ann_file='data/MOT20/annotations/half-val_cocoformat.json',
+        img_prefix='data/MOT20/train',
+        pipeline=test_pipeline))
+
+checkpoint_config = dict(interval=1)
+evaluation = dict(metric=['bbox', 'track'], interval=100)

mmtrack/models/mot/__init__.py

@@ -2,9 +2,11 @@
 from .base import BaseMultiObjectTracker
 from .byte_track import ByteTrack
 from .deep_sort import DeepSORT
+from .ocsort import OCSORT
 from .qdtrack import QDTrack
 from .tracktor import Tracktor
 
 __all__ = [
-    'BaseMultiObjectTracker', 'Tracktor', 'DeepSORT', 'ByteTrack', 'QDTrack'
+    'BaseMultiObjectTracker', 'Tracktor', 'DeepSORT', 'ByteTrack', 'QDTrack',
+    'OCSORT'
 ]