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