SORT Tracker¶

Class: SORTBlockV1

Source: inference.core.workflows.core_steps.trackers.sort.v1.SORTBlockV1

Track objects across video frames using the SORT algorithm from the roboflow/trackers package.

SORT pairs a Kalman filter motion model with single-stage IoU-based Hungarian assignment. It has the fewest parameters and lowest overhead, processing hundreds of frames per second. However, it lacks re-identification and occlusion-recovery mechanisms, so tracks may fragment or switch IDs when objects are temporarily hidden.

When to use SORT: - Controlled environments with reliable, high-confidence detections. - Real-time pipelines where maximum throughput is critical. - Simple scenes with minimal occlusion and predictable linear motion.

When to consider alternatives: - If you see fragmented tracks or missed weak detections, try ByteTrack. - If objects undergo heavy occlusion or non-linear motion, try OC-SORT.

Outputs three detection sets: - tracked_detections: All confirmed tracked detections with assigned track IDs. - new_instances: Detections whose track ID appears for the first time. - already_seen_instances: Detections whose track ID has been seen in a prior frame.

The block maintains separate tracker state and instance cache per video_identifier, enabling multi-stream tracking within a single workflow.

Type identifier¶

Use the following identifier in step "type" field: roboflow_core/trackers_sort@v1to add the block as as step in your workflow.

Properties¶

Name	Type	Description	Refs
`name`	`str`	Enter a unique identifier for this step..	❌
`minimum_iou_threshold`	`float`	Minimum IoU required to associate a detection with an existing track. Default: 0.3..	✅
`minimum_consecutive_frames`	`int`	Number of consecutive frames a track must be matched before it is emitted as a confirmed track (tracker_id != -1). Default: 3..	✅
`lost_track_buffer`	`int`	Number of frames to keep a track alive after it loses its matched detection. Higher values improve occlusion recovery. Default: 30..	✅
`track_activation_threshold`	`float`	Minimum detection confidence required to spawn a new track. Detections below this threshold are not used to create new tracks. Default: 0.25..	✅
`instances_cache_size`	`int`	Maximum number of track IDs retained in the instance cache for new/already-seen categorisation. Uses FIFO eviction. Default: 16384..	❌

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections¶

Compatible Blocks

Check what blocks you can connect to SORT Tracker in version v1.

Input and Output Bindings¶

The available connections depend on its binding kinds. Check what binding kinds SORT Tracker in version v1 has.

Bindings

input
- image (image): Input image with embedded video metadata (fps and video_identifier). Used to initialise and retrieve per-video tracker state..
- detections (Union[instance_segmentation_prediction, object_detection_prediction, keypoint_detection_prediction]): Detection predictions for the current frame to track..
- minimum_iou_threshold (float_zero_to_one): Minimum IoU required to associate a detection with an existing track. Default: 0.3..
- minimum_consecutive_frames (integer): Number of consecutive frames a track must be matched before it is emitted as a confirmed track (tracker_id != -1). Default: 3..
- lost_track_buffer (integer): Number of frames to keep a track alive after it loses its matched detection. Higher values improve occlusion recovery. Default: 30..
- track_activation_threshold (float_zero_to_one): Minimum detection confidence required to spawn a new track. Detections below this threshold are not used to create new tracks. Default: 0.25..
output
- tracked_detections (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
- new_instances (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.
- already_seen_instances (object_detection_prediction): Prediction with detected bounding boxes in form of sv.Detections(...) object.

Example JSON definition of step SORT Tracker in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/trackers_sort@v1",
    "image": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "minimum_iou_threshold": 0.3,
    "minimum_consecutive_frames": 3,
    "lost_track_buffer": 30,
    "track_activation_threshold": 0.25,
    "instances_cache_size": "<block_does_not_provide_example>"
}