We provide lots of useful tools under the tools/ directory.

MOT Test-time Parameter Search¶

tools/analysis_tools/mot/mot_param_search.py can search the parameters of the tracker in MOT models. It is used as the same manner with tools/test.py but different in the configs.

Here is an example that shows how to modify the configs:

Define the desirable evaluation metrics to record.

For example, you can define the evaluator as
```
test_evaluator=dict(type='MOTChallengeMetrics', metric=['HOTA', 'CLEAR', 'Identity'])
```
Of course, you can also customize the content of metric in test_evaluator. You are free to choose one or more of ['HOTA', 'CLEAR', 'Identity'].

Define the parameters and the values to search.

Assume you have a tracker like

model=dict(
    tracker=dict(
        type='BaseTracker',
        obj_score_thr=0.5,
        match_iou_thr=0.5
    )
)

If you want to search the parameters of the tracker, just change the value to a list as follow

model=dict(
    tracker=dict(
        type='BaseTracker',
        obj_score_thr=[0.4, 0.5, 0.6],
        match_iou_thr=[0.4, 0.5, 0.6, 0.7]
    )
)

Then the script will test the totally 12 cases and log the results.

MOT Error Visualize¶

tools/analysis_tools/mot/mot_error_visualize.py can visualize errors for multiple object tracking. This script needs the result of inference. By Default, the red bounding box denotes false positive, the yellow bounding box denotes the false negative and the blue bounding box denotes ID switch.

python tools/analysis_tools/mot/mot_error_visualize.py \
    ${CONFIG_FILE}\
    --input ${INPUT} \
    --result-dir ${RESULT_DIR} \
    [--output-dir ${OUTPUT}] \
    [--fps ${FPS}] \
    [--show] \
    [--backend ${BACKEND}]

The RESULT_DIR contains the inference results of all videos and the inference result is a txt file.

Optional arguments:

OUTPUT: Output of the visualized demo. If not specified, the --show is obligate to show the video on the fly.
FPS: FPS of the output video.
--show: Whether show the video on the fly.
BACKEND: The backend to visualize the boxes. Options are cv2 and plt.

Browse dataset¶

tools/analysis_tools/mot/browse_dataset.py can visualize the training dataset to check whether the dataset configuration is correct.

Examples:

python tools/analysis_tools/browse_dataset.py ${CONFIG_FILE} [--show-interval ${SHOW_INTERVAL}]

Optional arguments:

SHOW_INTERVAL: The interval of show (s).
--show: Whether show the images on the fly.