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

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

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

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

• 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

• 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

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

• 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

• 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.