> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/ai-blocks/object-detection-tracker.md).

# Object\_Detection\_Tracker

This function block tracks objects across frames using detection results you provide. It assigns consistent IDs to moving objects, maintains a short history for each track (including a stable class label when available), and outputs an annotated image plus lists you can use for counting, logging, or higher-level analytics.

## 📥 Inputs <a href="#inputs" id="inputs"></a>

`Input Image` The current image frame used for visualization and tracking context.

`Detected Rectangles` A list of detected bounding boxes. Provide the detection rectangles generated by an object detection function block.

`Detected Classes` A list of class names corresponding to the provided detections. Used to build a stable class label per tracked object.

Note: These are input sockets. Feed detections from an object detection block into these sockets.

## 📤 Outputs <a href="#outputs" id="outputs"></a>

`Result Image` An image annotated with bounding boxes, IDs and class labels for each active track.

`Position - ID List` A list of tracked object center positions with their assigned IDs and stable class names.

`Rectangle - ID List` A list of bounding rectangles paired with assigned IDs and stable class names.

Note: These are output sockets. Use them to visualize, count, or forward tracking data to other blocks.

## 🕹️ Controls <a href="#controls" id="controls"></a>

This function block has no user-facing controls on the block itself. Behavior is driven by the detection inputs you provide and by how upstream blocks configure detection sensitivity and frequency.

## ⚙️ Running mechanism <a href="#how-it-works" id="how-it-works"></a>

* The block receives per-frame detections (rectangles and classes) together with the current frame image.
* It associates new detections with existing tracks to keep identities consistent across frames.
* For each track the block maintains a short history of class detections so that the displayed class becomes stable (it avoids quick class flipping due to momentary misclassifications).
* The block also tolerates short misses (temporary frames without detections) so tracks do not disappear immediately, and it removes tracks that remain inactive for a longer time.
* Outputs include an annotated image for visualization and structured lists (positions and rectangles paired with IDs and class names) for downstream processing.

This behavior is automatic; you control tracking result quality mainly by the quality and frequency of incoming detections.

## 🎯 Key features <a href="#features" id="features"></a>

* Stable ID assignment across frames for each detected object.
* Per-track class stabilization so displayed labels become consistent over time.
* Outputs both visualized results and structured lists for analytics or logging.
* Robust to short detection dropouts (temporary missed detections).
* Creates new tracks for unmatched detections and retires stale tracks automatically.

## 📝 Usage instructions <a href="#usage" id="usage"></a>

1. Connect an image source to the `Input Image` socket (examples: USB camera, IP camera, stream or preloaded frames).
2. Connect a detection block to supply rectangles and class names into `Detected Rectangles` and `Detected Classes` sockets.
3. Use the `Result Image` to preview the tracking output.
4. Use the `Position - ID List` and `Rectangle - ID List` outputs to feed downstream blocks for counting, logging or analytics.

## 💡 Tips and Tricks <a href="#tips-and-tricks" id="tips-and-tricks"></a>

* For best results, feed this block with reliable detections from an object detection block such as `Object Detection`, `Object Detection - Custom`, or `Object Detection (D-FINE)`.
* To visualize detections before/after tracking, connect `Result Image` to a `Show Image` or `Draw Detections` block.
* To record visual results, send `Result Image` to `Image Logger` or `Record Video`.
* To focus tracking on a specific area, crop the input first with `Image ROI`, `Image ROI Select` or `Image ROI Polygon` before feeding detections.
* To filter false detections or to count only objects inside a region, combine this block's `Rectangle - ID List` with `Rectangles in Rectangle` or `Check Area (Polygon)`.
* Use the `Rectangle - ID List` output to feed higher-level applications such as `Traffic Intersection Analysis` for traffic counting or area-crossing events.
* If detections are noisy, try adding preprocessing like `Blur`, `Image Threshold` or `HSV Filter` upstream to improve detection quality before tracking.

## 🛠️ Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

* No tracks appear: Verify that the `Detected Rectangles` and `Detected Classes` sockets receive valid detection data from an object detection block.
* IDs change rapidly or labels flip often: Improve detection stability (better detector, preprocessing) or reduce detection noise so the block can build a stable class history.
* Tracks disappear too quickly: Ensure detections are provided for consecutive frames; if you have intermittent detections, consider smoothing or increasing detection frequency upstream.
* Lots of short-lived tracks: Try filtering small or low-confidence detections before sending them in, or use region cropping (e.g., `Image ROI Select`) to reduce false positives.

If you need a quick example setup: USB camera (`Camera USB`) -> an object detection block (for example `Object Detection (D-FINE)`) -> `Object_Detection_Tracker` -> `Show Image` / `Image Logger` / `Traffic Intersection Analysis`.