Delta Filter¶
Class: DeltaFilterBlockV1
Source: inference.core.workflows.core_steps.flow_control.delta_filter.v1.DeltaFilterBlockV1
Trigger workflow execution only when an input value changes from its previous state, enabling change detection, avoiding redundant processing when values remain constant, and optimizing system efficiency by executing downstream steps only on state transitions.
How This Block Works¶
This block monitors a value and only continues workflow execution when that value changes compared to its previous state. The block:
- Takes an image (for video metadata context) and a value to monitor as input
- Extracts video metadata from the image to identify the video stream (video_identifier)
- Retrieves the previously cached value for this video identifier from an internal cache
- Compares the current input value against the cached previous value
- If the value has changed (current value ≠ previous value):
- Updates the cache with the new value for this video identifier
- Continues execution to the specified
next_stepsblocks, allowing downstream processing - If the value has not changed (current value == previous value):
- Terminates the current workflow branch, preventing redundant downstream execution
- Returns flow control directives that either continue to next steps or terminate the branch
The block maintains separate cached values for each video stream (identified by video_identifier), allowing it to track value changes independently across multiple video sources. This per-video tracking ensures that the filter resets appropriately when switching between different video streams. The block supports monitoring any value type (numbers, strings, detection counts, etc.), making it versatile for detecting changes in counters, metrics, detection results, or any other workflow data. By only triggering downstream blocks when values actually change, the Delta Filter prevents unnecessary processing when values remain constant, which is especially useful in video workflows where many frames may have the same detection count or metric value.
Common Use Cases¶
- Change Detection for Counters: Trigger actions only when counter values change (e.g., execute data logging when line counter count_in changes from 5 to 6, skip processing when count remains at 6), avoiding redundant writes or updates when values are stable
- State Transition Monitoring: Detect transitions in system states or detection results and trigger workflows only on state changes (e.g., execute notification when detection class changes from "empty" to "occupied", skip when state remains "occupied"), preventing repeated actions for the same state
- Conditional Data Logging: Write to databases, CSV files, or external systems only when values change (e.g., log count changes to OPC or PLC systems, skip logging when counts are unchanged), reducing storage and network overhead
- Event-Based Notifications: Send alerts or notifications only when values transition (e.g., trigger email notification when zone count changes, avoid spam when count remains constant), ensuring notifications represent meaningful changes rather than repeated states
- Optimized Processing Pipelines: Reduce computational load in video workflows by skipping downstream processing when monitored values haven't changed (e.g., skip expensive analysis when detection count is unchanged across frames), improving overall workflow efficiency
- Multi-Stream Change Tracking: Monitor value changes independently across multiple video streams (e.g., track zone counts separately for different camera feeds), with automatic per-video caching ensuring correct change detection for each stream
Connecting to Other Blocks¶
This block monitors values and controls workflow execution flow, and can be connected:
- After counting or metric blocks (e.g., Line Counter, Time in Zone, Velocity, Detection Filter) to detect when counts, metrics, or aggregated values change and conditionally trigger downstream processing based on value transitions
- After detection blocks (e.g., Object Detection, Classification, Keypoint Detection) to monitor detection results, class changes, or confidence metrics and execute actions only when detection outcomes change from previous frames
- After data processing blocks (e.g., Property Definition, Expression, Delta Filter) to track computed values or processed metrics and trigger workflows only when these computed values transition, avoiding redundant processing
- Before data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload, Webhook Sink) to conditionally log or store data only when monitored values change, preventing duplicate entries or unnecessary writes when values remain constant
- Before notification blocks (e.g., Email Notification, Slack Notification, Twilio SMS Notification) to trigger alerts only when meaningful changes occur (e.g., count changes, state transitions), avoiding notification spam when values are stable
- In video processing workflows where per-frame values may remain constant for many frames, using the block to efficiently detect changes and trigger expensive downstream operations only when necessary, optimizing resource usage
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/delta_filter@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
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 Delta Filter in version v1.
- inputs:
Dynamic Crop,Barcode Detection,OCR Model,Image Blur,Background Subtraction,Google Vision OCR,Environment Secrets Store,Google Gemini,Image Preprocessing,Local File Sink,Object Detection Model,Single-Label Classification Model,Bounding Box Visualization,Model Monitoring Inference Aggregator,Multi-Label Classification Model,Keypoint Detection Model,Camera Focus,Identify Outliers,Dot Visualization,Florence-2 Model,Gaze Detection,Roboflow Dataset Upload,CSV Formatter,Depth Estimation,Polygon Visualization,OpenAI,Line Counter,Detections List Roll-Up,Image Slicer,Rate Limiter,Heatmap Visualization,Google Gemini,Distance Measurement,Line Counter Visualization,Morphological Transformation,Stability AI Image Generation,Keypoint Visualization,Keypoint Detection Model,Background Color Visualization,Label Visualization,QR Code Detection,Polygon Visualization,LMM,CogVLM,Time in Zone,Single-Label Classification Model,Triangle Visualization,Stability AI Outpainting,Mask Visualization,Color Visualization,Detections Combine,Dominant Color,Text Display,Bounding Rectangle,Reference Path Visualization,OpenAI,Llama 3.2 Vision,Image Threshold,Clip Comparison,Clip Comparison,Classification Label Visualization,Polygon Zone Visualization,Delta Filter,Image Contours,VLM As Classifier,Continue If,Roboflow Custom Metadata,Dynamic Zone,LMM For Classification,Velocity,Halo Visualization,Semantic Segmentation Model,Blur Visualization,Path Deviation,Absolute Static Crop,Anthropic Claude,Cosine Similarity,SAM 3,Detections Transformation,Ellipse Visualization,Identify Changes,Crop Visualization,Path Deviation,SIFT Comparison,Trace Visualization,Twilio SMS Notification,Size Measurement,Detections Stabilizer,Stitch Images,Detections Merge,Time in Zone,Motion Detection,Email Notification,OpenAI,SIFT Comparison,Qwen2.5-VL,Seg Preview,Time in Zone,Instance Segmentation Model,Anthropic Claude,Multi-Label Classification Model,Detections Stitch,Email Notification,Slack Notification,Twilio SMS/MMS Notification,VLM As Detector,Cache Set,Camera Focus,SAM 3,Stitch OCR Detections,Perspective Correction,Moondream2,PTZ Tracking (ONVIF),Expression,Camera Calibration,Corner Visualization,Icon Visualization,Qwen3.5-VL,Overlap Filter,Byte Tracker,VLM As Detector,Halo Visualization,JSON Parser,Detection Event Log,Pixelate Visualization,Qwen3-VL,Contrast Equalization,Dimension Collapse,VLM As Classifier,Data Aggregator,Instance Segmentation Model,Detections Classes Replacement,Relative Static Crop,Line Counter,Stitch OCR Detections,First Non Empty Or Default,Circle Visualization,Webhook Sink,Image Convert Grayscale,Grid Visualization,Byte Tracker,Mask Area Measurement,Buffer,Florence-2 Model,SmolVLM2,SAM 3,Perception Encoder Embedding Model,Cache Get,SIFT,YOLO-World Model,Object Detection Model,Byte Tracker,Detections Consensus,Template Matching,Property Definition,Anthropic Claude,Google Gemini,Model Comparison Visualization,Detection Offset,EasyOCR,QR Code Generator,Image Slicer,S3 Sink,CLIP Embedding Model,Stability AI Inpainting,Segment Anything 2 Model,Detections Filter,OpenAI,Pixel Color Count,Roboflow Dataset Upload - outputs: None
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Delta Filter in version v1 has.
Bindings
-
input
image(image): not available.value(*): Value to monitor for changes. Can be any data type (numbers, strings, detection counts, metrics, etc.) from workflow inputs or step outputs. The workflow branch continues to next_steps only when this value differs from the previously cached value for the current video stream. If the value remains the same, the branch terminates to avoid redundant processing. Example: Monitor a line counter count ($steps.line_counter.count_in) and trigger actions only when the count changes..next_steps(step): List of workflow steps to execute when the monitored value changes from its previous state. These steps receive control flow only when a change is detected, allowing conditional downstream processing. If the value hasn't changed, these steps will not execute as the branch terminates. Each step selector references a block in the workflow that should execute on value transitions..
-
output
Example JSON definition of step Delta Filter in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/delta_filter@v1",
"image": "<block_does_not_provide_example>",
"value": "$steps.line_counter.count_in",
"next_steps": "$steps.write_to_csv"
}