models ocsort_yolox_x_crowdhuman_mot17 private half

ocsort_yolox_x_crowdhuman_mot17-private-half

Overview

ocsort_yolox_x_crowdhuman_mot17-private-half model is from OpenMMLab's MMTracking library. This model is reported to obtain MOTA: 77.8, IDF1: 78.4 for video-multi-object-tracking task on MOT17-half-eval dataset.

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.

The above abstract is from MMTracking website. Review the original-model-card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model.

Inference samples

Inference type	Python sample (Notebook)	CLI with YAML
Real time	video-multi-object-tracking-online-endpoint.ipynb	video-multi-object-tracking-online-endpoint.sh

Finetuning samples

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Video multi-object tracking	Video multi-object tracking	MOT17 tiny	mot17-tiny-video-multi-object-tracking.ipynb	mot17-tiny-video-multi-object-tracking.sh

Sample inputs and outputs (for real-time inference)

Sample input

{
  "input_data": {
    "columns": [
      "video"
    ],
    "data": ["video_link"]
  }
}

Note: "video_link" should be a publicly accessible url.

Sample output

[
  {
    "det_bboxes": [
      {
        "box": {
          "topX": 703.9149780273,
          "topY": -5.5951070786,
          "bottomX": 756.9875488281,
          "bottomY": 158.1963806152
        },
        "label": 0,
        "score": 0.9597821236
      },
      {
        "box": {
          "topX": 1487.9072265625,
          "topY": 67.9468841553,
          "bottomX": 1541.1591796875,
          "bottomY": 217.5476837158
        },
        "label": 0,
        "score": 0.9568068385
      }
    ],
    "track_bboxes": [
      {
        "box": {
          "instance_id": 0,
          "topX": 703.9149780273,
          "topY": -5.5951070786,
          "bottomX": 756.9875488281,
          "bottomY": 158.1963806152
        },
        "label": 0,
        "score": 0.9597821236
      },
      {
        "box": {
          "instance_id": 1,
          "topX": 1487.9072265625,
          "topY": 67.9468841553,
          "bottomX": 1541.1591796875,
          "bottomY": 217.5476837158
        },
        "label": 0,
        "score": 0.9568068385
      }
    ],
    "frame_id": 0,
    "video_url": "video_link"
  }
]

Model inference - visualization for a video

Version: 4

Tags

Preview license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'false', 'apply_ort': 'false'}) task : multi-object-tracking openmmlab_model_id : ocsort_yolox_x_crowdhuman_mot17-private-half SharedComputeCapacityEnabled inference_compute_allow_list : ['Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC4as_T4_v3', 'Standard_NC64as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND40rs_v2', 'Standard_ND96amsr_A100_v4', 'Standard_ND96asr_v4']

View in Studio: https://ml.azure.com/registries/azureml/models/ocsort_yolox_x_crowdhuman_mot17-private-half/version/4

License: apache-2.0

Properties

finetune-min-sku-spec: 4|1|28|176

finetune-recommended-sku: Standard_NC6s_v3

finetuning-tasks: video-multi-object-tracking

inference-min-sku-spec: 2|1|14|28

inference-recommended-sku: Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC16as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC4as_T4_v3, Standard_NC64as_T4_v3, Standard_NC8as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND40rs_v2, Standard_ND96amsr_A100_v4, Standard_ND96asr_v4