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:
Stability AI Outpainting,SAM 3,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Corner Visualization,Ellipse Visualization,Seg Preview,Roboflow Vision Events,Object Detection Model,Heatmap Visualization,Trace Visualization,OC-SORT Tracker,VLM As Classifier,Time in Zone,OpenAI,Email Notification,Byte Tracker,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,YOLO-World Model,JSON Parser,Polygon Zone Visualization,Byte Tracker,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,Stitch Images,OpenRouter,Image Blur,Model Monitoring Inference Aggregator,Dynamic Zone,Clip Comparison,Detections Stitch,Time in Zone,Detections List Roll-Up,Segment Anything 2 Model,Instance Segmentation Model,Google Gemini,Buffer,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Local File Sink,Florence-2 Model,Icon Visualization,Detection Offset,Image Contours,SAM2 Video Tracker,Single-Label Classification Model,OpenAI,Detections Filter,SAM 3,Grid Visualization,ByteTrack Tracker,VLM As Detector,Size Measurement,Object Detection Model,Detections Transformation,LMM,Image Convert Grayscale,Reference Path Visualization,Stitch OCR Detections,Keypoint Detection Model,SIFT Comparison,Identify Changes,Roboflow Dataset Upload,CSV Formatter,S3 Sink,BoT-SORT Tracker,SIFT Comparison,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Identify Outliers,Crop Visualization,Blur Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Stitch OCR Detections,Velocity,Google Gemma API,Image Slicer,Qwen 3.5 API,Path Deviation,Background Color Visualization,Slack Notification,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Detection Event Log,Bounding Rectangle,Path Deviation,CogVLM,Llama 3.2 Vision,Instance Segmentation Model,Qwen3.5-VL,Camera Focus,Instance Segmentation Model,Google Vision OCR,Google Gemini,SAM 3,Llama 3.2 Vision,Distance Measurement,SORT Tracker,Twilio SMS Notification,Detections Stabilizer,Moondream2,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Gaze Detection,Template Matching,Classification Label Visualization,PTZ Tracking (ONVIF),Florence-2 Model,MoonshotAI Kimi,Time in Zone,MoonshotAI Kimi,Line Counter,Dot Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Stability AI Inpainting,Per-Class Confidence Filter,Line Counter,Detections Classes Replacement,Label Visualization,Overlap Filter,Detections Merge,Absolute Static Crop,Google Gemini,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Pixel Color Count,Clip Comparison,LMM For Classification,Text Display,Mask Area Measurement,Circle Visualization,Line Counter Visualization,Byte Tracker,OCR Model,Detections Combine,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Mask Edge Snap,Triangle Visualization,Perspective Correction - outputs:
Stability AI Outpainting,Multi-Label Classification Model,CLIP Embedding Model,SAM 3,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Seg Preview,Ellipse Visualization,Corner Visualization,Roboflow Vision Events,Object Detection Model,Heatmap Visualization,Trace Visualization,OC-SORT Tracker,VLM As Classifier,Time in Zone,OpenAI,Byte Tracker,Keypoint Visualization,Model Comparison Visualization,YOLO-World Model,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Image Blur,Clip Comparison,Detections Stitch,Segment Anything 2 Model,Instance Segmentation Model,Google Gemini,Buffer,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Qwen2.5-VL,Keypoint Detection Model,Florence-2 Model,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,SAM2 Video Tracker,SAM 3,ByteTrack Tracker,VLM As Detector,Barcode Detection,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Dominant Color,Keypoint Detection Model,SIFT Comparison,Roboflow Dataset Upload,BoT-SORT Tracker,Qwen3-VL,Object Detection Model,Morphological Transformation,Crop Visualization,Blur Visualization,Qwen-VL,Mask Visualization,Stability AI Image Generation,Google Gemma API,Qwen3.5,Image Slicer,Qwen 3.5 API,Perception Encoder Embedding Model,Background Color Visualization,Anthropic Claude,Qwen 3.6 API,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Llama 3.2 Vision,CogVLM,Instance Segmentation Model,Qwen3.5-VL,Instance Segmentation Model,Google Vision OCR,Camera Focus,Google Gemini,Llama 3.2 Vision,SAM 3,Single-Label Classification Model,SORT Tracker,SmolVLM2,Detections Stabilizer,Moondream2,Anthropic Claude,Image Slicer,OpenAI,Depth Estimation,Multi-Label Classification Model,Gaze Detection,Template Matching,Classification Label Visualization,Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Dot Visualization,Keypoint Detection Model,Background Subtraction,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,QR Code Detection,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Halo Visualization,Email Notification,Camera Calibration,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Line Counter Visualization,Circle Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Mask Edge Snap,Triangle Visualization,Perspective Correction
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[keypoint_detection_prediction,rle_instance_segmentation_prediction,instance_segmentation_prediction,object_detection_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
}