Trace Visualization¶
Class: TraceVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.trace.v1.TraceVisualizationBlockV1
Draw trajectory paths for tracked objects, visualizing their movement history by connecting recent positions with colored lines to show object movement patterns, paths, and tracking behavior over time.
How This Block Works¶
This block takes an image and tracked predictions (with tracker IDs) and draws trajectory paths showing the recent movement history of each tracked object. The block:
- Takes an image and tracked predictions as input (predictions must include tracker_id data from a tracking block)
- Extracts tracking IDs and position history for each tracked object
- Determines the reference point for drawing traces based on the selected position anchor (center, corners, edges, or center of mass)
- Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
- Draws trajectory lines connecting the recent positions (up to trace_length positions) for each tracked object using Supervision's TraceAnnotator
- Connects historical positions sequentially, creating path traces that show object movement direction and patterns
- Returns an annotated image with trajectory paths overlaid on the original image
The block visualizes object tracking by drawing the path that each tracked object has taken over recent frames. Each tracked object gets a unique trace line (colored by track ID, class, or index) that connects its recent positions, creating a visual trail that shows movement direction, speed, and trajectory patterns. The trace_length parameter controls how many historical positions are included in each trace (longer traces show more movement history, shorter traces show recent movement only). This visualization requires predictions with tracker IDs from tracking blocks (like Byte Tracker), as it needs the tracking information to connect positions across frames. The traces help visualize object movement, identify tracking patterns, and understand object behavior over time.
Common Use Cases¶
- Object Trajectory Visualization: Visualize movement paths and trajectories of tracked objects to understand object behavior, movement patterns, or navigation routes for applications like vehicle tracking, pedestrian flow analysis, or object movement monitoring
- Tracking Performance Validation: Validate tracking performance by visualizing object paths to ensure tracking consistency, identify tracking errors or ID switches, or verify that objects maintain consistent trajectories
- Movement Pattern Analysis: Analyze movement patterns, speeds, or direction changes by visualizing trajectory traces to understand object behavior, detect anomalies, or identify movement trends in surveillance, security, or traffic monitoring workflows
- Path Deviation Detection: Visualize object paths to detect deviations from expected routes, identify unusual movement patterns, or monitor object trajectories for safety, security, or compliance workflows
- Real-Time Tracking Monitoring: Display trajectory traces in real-time monitoring interfaces, dashboards, or live video feeds to visualize object movement and tracking behavior as it happens
- Video Analysis and Post-Processing: Create trajectory visualizations for video analysis, post-processing workflows, or forensic analysis where understanding object movement paths and patterns is critical
Connecting to Other Blocks¶
The annotated image from this block can be connected to:
- Tracking blocks (e.g., Byte Tracker) to receive tracked predictions with tracker IDs that are required for trace visualization
- Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Dot Visualization) to combine trajectory traces with additional annotations for comprehensive tracking visualization
- Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with trajectory traces for documentation, reporting, or analysis
- Webhook blocks to send visualized results with trajectory traces to external systems, APIs, or web applications for display in dashboards or monitoring tools
- Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with trajectory traces as visual evidence in alerts or reports
- Video output blocks to create annotated video streams or recordings with trajectory traces for live monitoring, tracking visualization, or post-processing analysis
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/trace_visualization@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
copy_image |
bool |
Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations.. | ✅ |
color_palette |
str |
Select a color palette for the visualised elements.. | ✅ |
palette_size |
int |
Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes.. | ✅ |
custom_colors |
List[str] |
Define a list of custom colors for bounding boxes in HEX format.. | ✅ |
color_axis |
str |
Choose how bounding box colors are assigned.. | ✅ |
position |
str |
Anchor position for drawing trajectory traces relative to each detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object). The trace path is drawn connecting positions at this anchor point across recent frames.. | ✅ |
trace_length |
int |
Maximum number of historical tracked object positions to include in each trajectory trace. Controls how long the movement trail appears. Higher values create longer traces showing more movement history, while lower values create shorter traces showing only recent movement. Must be at least 1. Typical values range from 10 to 50 frames depending on the desired trail length and frame rate.. | ✅ |
thickness |
int |
Thickness of the trajectory trace lines in pixels. Controls how thick the path lines appear. Higher values create thicker, more visible traces, while lower values create thinner, more subtle traces. Must be at least 1. Typical values range from 1 to 5 pixels.. | ✅ |
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 Trace Visualization in version v1.
- inputs:
Roboflow Dataset Upload,Line Counter Visualization,Mask Edge Snap,OCR Model,Image Slicer,Gaze Detection,Instance Segmentation Model,Distance Measurement,Color Visualization,Bounding Rectangle,Ellipse Visualization,Polygon Visualization,ByteTrack Tracker,Relative Static Crop,Byte Tracker,Detections Consensus,Detections Classes Replacement,Webhook Sink,Trace Visualization,Object Detection Model,Camera Focus,Stitch OCR Detections,Qwen 3.5 API,OpenAI,Buffer,SAM 3,Size Measurement,Image Threshold,Heatmap Visualization,SORT Tracker,Florence-2 Model,Halo Visualization,Detections Transformation,Path Deviation,GLM-OCR,Dot Visualization,S3 Sink,Path Deviation,Twilio SMS Notification,Seg Preview,Model Monitoring Inference Aggregator,Google Gemini,Roboflow Dataset Upload,Dynamic Zone,Clip Comparison,VLM As Classifier,Pixelate Visualization,Line Counter,Twilio SMS/MMS Notification,Polygon Zone Visualization,Motion Detection,Blur Visualization,Background Subtraction,Text Display,CSV Formatter,Stability AI Image Generation,Detections Merge,Perspective Correction,Overlap Filter,Anthropic Claude,Bounding Box Visualization,Velocity,Depth Estimation,Line Counter,Stability AI Inpainting,Polygon Visualization,SIFT,Roboflow Vision Events,VLM As Detector,Google Gemini,Label Visualization,Grid Visualization,Qwen3.5-VL,Contrast Equalization,Per-Class Confidence Filter,Triangle Visualization,Halo Visualization,Circle Visualization,Segment Anything 2 Model,Mask Visualization,OpenAI,MoonshotAI Kimi,Llama 3.2 Vision,Email Notification,Slack Notification,Detections Stitch,Detections Stabilizer,Object Detection Model,Stability AI Outpainting,Email Notification,Google Gemma API,Google Vision OCR,Identify Outliers,Image Preprocessing,Google Gemini,EasyOCR,Detections Combine,Object Detection Model,SAM2 Video Tracker,Detection Event Log,Byte Tracker,OpenAI,Anthropic Claude,Time in Zone,Model Comparison Visualization,Roboflow Custom Metadata,YOLO-World Model,Detection Offset,Instance Segmentation Model,Single-Label Classification Model,VLM As Classifier,Detections List Roll-Up,Template Matching,Mask Area Measurement,Stitch Images,Qwen 3.6 API,SIFT Comparison,Morphological Transformation,Instance Segmentation Model,CogVLM,Crop Visualization,Camera Calibration,Florence-2 Model,Time in Zone,OC-SORT Tracker,SAM 3,Icon Visualization,Local File Sink,Detections Filter,Image Contours,JSON Parser,Keypoint Detection Model,Time in Zone,Reference Path Visualization,Dimension Collapse,Anthropic Claude,Clip Comparison,VLM As Detector,LMM,Pixel Color Count,Identify Changes,Classification Label Visualization,Image Slicer,Absolute Static Crop,Image Blur,Byte Tracker,Multi-Label Classification Model,Image Convert Grayscale,SAM 3,OpenAI,Corner Visualization,Dynamic Crop,Moondream2,Keypoint Visualization,Keypoint Detection Model,QR Code Generator,Camera Focus,LMM For Classification,Morphological Transformation,Keypoint Detection Model,Contrast Enhancement,Background Color Visualization,PTZ Tracking (ONVIF),Stitch OCR Detections,SIFT Comparison - outputs:
Roboflow Dataset Upload,Line Counter Visualization,Mask Edge Snap,OCR Model,Image Slicer,Gaze Detection,Qwen2.5-VL,Instance Segmentation Model,Color Visualization,Multi-Label Classification Model,Ellipse Visualization,Polygon Visualization,ByteTrack Tracker,Single-Label Classification Model,Relative Static Crop,Barcode Detection,Trace Visualization,Object Detection Model,Qwen 3.5 API,Camera Focus,OpenAI,Buffer,SAM 3,Image Threshold,Heatmap Visualization,SORT Tracker,Florence-2 Model,Halo Visualization,GLM-OCR,Dot Visualization,Semantic Segmentation Model,Seg Preview,Google Gemini,Roboflow Dataset Upload,Clip Comparison,VLM As Classifier,Pixelate Visualization,Twilio SMS/MMS Notification,Polygon Zone Visualization,Motion Detection,Blur Visualization,Background Subtraction,Text Display,Stability AI Image Generation,Perspective Correction,Anthropic Claude,Bounding Box Visualization,Depth Estimation,Stability AI Inpainting,Polygon Visualization,SmolVLM2,SIFT,Roboflow Vision Events,VLM As Detector,Google Gemini,Label Visualization,Qwen3.5-VL,Contrast Equalization,Triangle Visualization,Halo Visualization,Circle Visualization,Segment Anything 2 Model,Mask Visualization,Dominant Color,OpenAI,MoonshotAI Kimi,Llama 3.2 Vision,Email Notification,CLIP Embedding Model,Detections Stabilizer,Detections Stitch,Object Detection Model,Stability AI Outpainting,Google Gemma API,Google Vision OCR,Google Gemini,Image Preprocessing,EasyOCR,Object Detection Model,OpenAI,SAM2 Video Tracker,Byte Tracker,Anthropic Claude,Qwen3-VL,Model Comparison Visualization,YOLO-World Model,Instance Segmentation Model,Perception Encoder Embedding Model,Semantic Segmentation Model,Single-Label Classification Model,VLM As Classifier,Template Matching,Stitch Images,Qwen 3.6 API,SIFT Comparison,Morphological Transformation,Instance Segmentation Model,CogVLM,Crop Visualization,Florence-2 Model,Camera Calibration,Multi-Label Classification Model,Time in Zone,OC-SORT Tracker,SAM 3,QR Code Detection,Icon Visualization,Image Contours,Keypoint Detection Model,Reference Path Visualization,Anthropic Claude,Clip Comparison,VLM As Detector,LMM,Pixel Color Count,Multi-Label Classification Model,Image Slicer,Absolute Static Crop,Classification Label Visualization,Image Blur,Image Convert Grayscale,SAM 3,Single-Label Classification Model,OpenAI,Corner Visualization,Dynamic Crop,Keypoint Detection Model,Moondream2,Keypoint Visualization,Camera Focus,LMM For Classification,Morphological Transformation,Keypoint Detection Model,Contrast Enhancement,Background Color Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Trace Visualization in version v1 has.
Bindings
-
input
image(image): The image to visualize on..copy_image(boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..predictions(Union[object_detection_prediction,rle_instance_segmentation_prediction,keypoint_detection_prediction,instance_segmentation_prediction]): Model predictions to visualize..color_palette(string): Select a color palette for the visualised elements..palette_size(integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..custom_colors(list_of_values): Define a list of custom colors for bounding boxes in HEX format..color_axis(string): Choose how bounding box colors are assigned..position(string): Anchor position for drawing trajectory traces relative to each detection's bounding box. Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object). The trace path is drawn connecting positions at this anchor point across recent frames..trace_length(integer): Maximum number of historical tracked object positions to include in each trajectory trace. Controls how long the movement trail appears. Higher values create longer traces showing more movement history, while lower values create shorter traces showing only recent movement. Must be at least 1. Typical values range from 10 to 50 frames depending on the desired trail length and frame rate..thickness(integer): Thickness of the trajectory trace lines in pixels. Controls how thick the path lines appear. Higher values create thicker, more visible traces, while lower values create thinner, more subtle traces. Must be at least 1. Typical values range from 1 to 5 pixels..
-
output
image(image): Image in workflows.
Example JSON definition of step Trace Visualization in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/trace_visualization@v1",
"image": "$inputs.image",
"copy_image": true,
"predictions": "$steps.object_detection_model.predictions",
"color_palette": "DEFAULT",
"palette_size": 10,
"custom_colors": [
"#FF0000",
"#00FF00",
"#0000FF"
],
"color_axis": "CLASS",
"position": "CENTER",
"trace_length": 30,
"thickness": 1
}