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refactor and reduce false positives

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This commit is contained in:
Blake Blackshear
2020-09-07 12:17:42 -05:00
parent ea4ecae27c
commit acb75fa02d
10 changed files with 539 additions and 230 deletions
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13
config/config.example.yml
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@@ -53,9 +53,10 @@ mqtt:
# unless overridden at the camera levels.
# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
# All labels from the model are reported over MQTT. These values are used to filter out false positives.
# min_area (optional): minimum width*height of the bounding box for the detected person
# max_area (optional): maximum width*height of the bounding box for the detected person
# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
# min_area (optional): minimum width*height of the bounding box for the detected object
# max_area (optional): maximum width*height of the bounding box for the detected object
# min_score (optional): minimum score for the object
# threshold (optional): The minimum decimal percentage for tracked object's computed score to considered a true positive
####################
objects:
track:
@@ -66,7 +67,8 @@ objects:
person:
min_area: 5000
max_area: 100000
threshold: 0.8
min_score: 0.5
threshold: 0.85
zones:
#################
@@ -184,4 +186,5 @@ cameras:
person:
min_area: 5000
max_area: 100000
threshold: 0.8
min_score: 0.5
threshold: 0.85