Proximity Detector

Detect and create groups of objects when they are close to or far from each other.


The Proximity Detector node detects and creates groups of objects when they are close to or far from each other. This node makes it easy to build common use cases such as Object left behind, Crowd forming, Service times for drive throughs, etc.

This node requires a AI Model Node and a Track Objects Node before it in order to function properly.

Inputs & Outputs

  • Inputs : 1, Media Format : Raw Video
  • Outputs : 1, Media Format: Raw Video
  • Output Metadata : Presence Information


roisSemicolon separated list of areas in the video within which the Proximity Detector tracks these objects. If none is specified, the Proximity Detector tracks objects within the entire frame. Format: left1,top1,right1,bottom1; left2,top2,right2,bottom2 Ex: 10,10,100,100; 200,200,300,300
roi_labelsComma separated list of labels for each ROI in the rois list above. Format: label1, label2 Ex: door, window
proximity_groupsComma separated list of group types to detect. Format: A+B (Objects of Type A which have Type B nearby), A-B (Objects of Type A without any Type B nearby) Ex. car+person,person+person,bag-person, Accepted formats: object_label: any object of this type, with or without a classifier attribute. Example: car, object_label.class_type: any object of this type that has a specific classifier attribute. Example:, object_label.*: any object of this type that has at least one classifier attribute. Example: car.* (this will match, car.yellow, etc)
distance_thresholdPixel distance between two objects to be considered as within a '+' group or outside a '-' group. Range: 0-1. Specified as percentage of video source width.
merge_modeMerge groups of same or any type that overlap each other within the Group Distance Limit. Options: no_merge : Do not merge overlapping groups, same_type: Only merge groups of the same type. For example, will only merge two car+person groups that overlap, any_type : Merge groups of any type. For example, will merge car+person and car+bag groups if there's a car with a person and bag near it.
min_group_sizeIgnore groups smaller than this size.
group_size_trigger_thresholdSet a trigger when the group size exceeds this number.
min_group_lifespanIgnore groups that exist for less than this amount of time in seconds.
group_lifespan_trigger_thresholdSet a trigger when the group lifespan exceeds this time in seconds.
display_groups_modeDisplay groups that meet this criteria on the video. Options: none : None, above_threshold : Only those above the size or lifespan thresholds, all : All
display_propertiesDisplay these group properties if Display Groups Mode is not None. Options: basic : Basic includes Lifespan + Size, intermediate : Basic + Group type, detailed : Intermediate + Group ID
display_roiIf true, ROI will be drawn on video


Metadata PropertyDescription
nodes.<node_id>Describes the ROIs monitored by this node, and their properties. Format : as defined in the table below. <node_id> for Proximity Detector Nodes is of the form annotate_proximityX (ex. annotate_proximity1)


"nodes": {
    "annotate_proximity1": {
        "type": "annotate_proximity",
        "rois": {
            "<roi_label>": {
                "coords": [[<point1_x>, <point1_y>], 
                           [<point2_x>, <point2_y>], 
                           [<point3_x>, <point3_y>]],
                "groups_entered_delta": <int>,
                "groups_above_lifespan_threshold_count": <int>,
                "groups_above_lifespan_threshold_delta": <int>,
                "groups_above_size_threshold_count": <int>,
                "groups_above_size_threshold_delta": <int>,                  
                "current_group_count": <int>,
                "groups": [
                        "id": <group unique ID>,
                        "label": "<group type>",
                        "objects": [<object_id1>, <object_id2>, ....],
												"lifespan": <seconds>                      


<roi_label>Array of dictionariesContains information about this specific ROI
<roi_label>.coordsArray of ArraysContains the bounding box coordinates that represent this ROI
<roi_label>.current_group_countIntegerNumber of groups currently in this ROI
<roi_label>.groups_entered_deltaIntegerNumber of groups that entered the ROI between last frame and this frame
<roi_label>.groups_above_lifespan_threshold_countIntegerNumber of groups inside the ROI above the specified time threshold
<roi_label>.groups_above_lifespan_threshold_deltaIntegerNumber of new groups that exceeded the time threshold between the last frame and this frame
<roi_label>.groups_above_size_threshold_countIntegerNumber of groups inside the ROI above the specified size threshold
<roi_label>.groups_above_size_threshold_deltaIntegerNumber of new groups that exceeded the size threshold between the last frame and this frame
<roi_label>.groupsArray of dictionariesInformation about the groups currently in the ROI. id : Unique identifier for a specific group. label : Group Type (ex. car+person). objects : List of object IDs that make up this group, as obtained from Track Objects Node. lifespan : Time this group has been seen.