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:
Image Threshold,Email Notification,Corner Visualization,Roboflow Dataset Upload,Object Detection Model,Stitch OCR Detections,Dimension Collapse,Gaze Detection,Stability AI Image Generation,Time in Zone,Grid Visualization,Dynamic Crop,Image Slicer,Image Preprocessing,Instance Segmentation Model,SIFT,Line Counter Visualization,Detections Combine,Trace Visualization,Halo Visualization,ByteTrack Tracker,Cache Get,Roboflow Custom Metadata,Pixelate Visualization,Circle Visualization,Semantic Segmentation Model,S3 Sink,Detections Classes Replacement,Keypoint Detection Model,Twilio SMS Notification,Data Aggregator,Halo Visualization,SIFT Comparison,Anthropic Claude,OC-SORT Tracker,Detections Consensus,Polygon Visualization,Cosine Similarity,Identify Changes,Qwen3-VL,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Detection Offset,CLIP Embedding Model,Heatmap Visualization,Webhook Sink,Detections List Roll-Up,Cache Set,Google Vision OCR,Florence-2 Model,Florence-2 Model,Environment Secrets Store,VLM As Classifier,Overlap Filter,Anthropic Claude,OpenAI,VLM As Detector,OpenAI,PTZ Tracking (ONVIF),Bounding Rectangle,Background Color Visualization,Template Matching,Anthropic Claude,Background Subtraction,SIFT Comparison,Multi-Label Classification Model,Keypoint Visualization,Time in Zone,Detections Filter,Stitch OCR Detections,LMM,Detections Merge,Detections Transformation,Identify Outliers,Perception Encoder Embedding Model,SAM 3,Motion Detection,Dynamic Zone,Single-Label Classification Model,Seg Preview,Object Detection Model,Roboflow Vision Events,VLM As Classifier,Detections Stitch,Triangle Visualization,Distance Measurement,Google Gemini,Expression,Path Deviation,Image Contours,Model Comparison Visualization,Stability AI Outpainting,Image Slicer,Stitch Images,Image Blur,Barcode Detection,Ellipse Visualization,OpenAI,Time in Zone,Depth Estimation,EasyOCR,Absolute Static Crop,JSON Parser,Multi-Label Classification Model,CogVLM,Google Gemini,Continue If,Velocity,Relative Static Crop,Morphological Transformation,LMM For Classification,Detection Event Log,Dot Visualization,GLM-OCR,Model Monitoring Inference Aggregator,Keypoint Detection Model,Pixel Color Count,Image Convert Grayscale,Icon Visualization,QR Code Generator,First Non Empty Or Default,Detections Stabilizer,Camera Focus,SAM 3,OCR Model,Text Display,Qwen2.5-VL,Reference Path Visualization,Instance Segmentation Model,Llama 3.2 Vision,CSV Formatter,SORT Tracker,Byte Tracker,Label Visualization,Classification Label Visualization,Byte Tracker,Segment Anything 2 Model,Polygon Zone Visualization,Stability AI Inpainting,Rate Limiter,Google Gemini,Perspective Correction,SAM 3,Camera Calibration,Qwen3.5-VL,Size Measurement,Email Notification,Contrast Equalization,Line Counter,Path Deviation,SmolVLM2,Single-Label Classification Model,Delta Filter,Byte Tracker,Property Definition,Line Counter,Color Visualization,OpenAI,Dominant Color,QR Code Detection,Local File Sink,Mask Area Measurement,Clip Comparison,YOLO-World Model,Buffer,Clip Comparison,Twilio SMS/MMS Notification,Blur Visualization,Bounding Box Visualization,Camera Focus,Polygon Visualization,Moondream2,VLM As Detector,Slack Notification - 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"
}