Release Notes: Deep Learning Streamer (DL Streamer) Pipeline Framework Release 2026.1#

Version 2026.1#

Key 2026.1 highlights:#

DLStreamer Coding Agent: AI-assisted pipeline builder that translates natural-language descriptions into working DL Streamer Python apps or GStreamer command lines
GstAnalytics metadata migration: full-frame GstAnalytics support, keypoints switched to GstAnalytics API, migration to upstream GStreamer 1.28+ API
New elements: gvastreammux, gvaanalytics, g3dinference
New model support: YOLO classification, PaddleOCRv5, GETI semantic segmentation
New D3D11 backend for inference elements for Windows GPU processing
PyTorch Image Models (TIMM) model import script
Windows installer and expanded Windows sample coverage
gvawatermark enhancements: custom full-frame text, dynamic Gaussian blur sized to ROI, metadata-controlled drawing
Inference enhancements: CPU thread affinity parameter, skip-raw-tensors property, DepthConverter
Optimizer enhancements: time reduction, pause/resume, partial results, file output, cross-stream batch/nireq grouping, detection-based validation
Deep SORT tracker refactoring with enhanced detection and track structures
New Python samples: gvaanalytics, inference performance optimizations, custom frame selection for VLM queries
Dynamic ONVIF camera discovery with DLS pipeline startup
DLStreamer ONVIF package as installable Python wheel
Component updates: OpenVINO 2026.1, GStreamer 1.28.2, OpenCV 4.13.0, NPU driver 1.32.1, Ubuntu 24.04.4 kernel 6.17
Introduced support for Intel® Core™ Processors - series 3 (Wildcat Lake)
CI: self-hosted Kubernetes infrastructure, configurable tests, Fedora 41 image building

Deep Learning Streamer (DL Streamer) Pipeline Framework is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Intel® Distribution of OpenVINO™ Toolkit Inference Engine backend, across Intel® architecture, CPU, discrete GPU, integrated GPU and NPU.

The complete solution leverages:

Open source GStreamer* framework for pipeline management
GStreamer* plugins for input and output such as media files and real-time streaming from camera or network
Video decode and encode plugins, either CPU optimized plugins or GPU-accelerated plugins based on VAAPI
Deep Learning models converted from training frameworks TensorFlow*, Caffe* etc.

The following elements in the Pipeline Framework repository:

Element	Description
gvaanalytics	Provides a framework for custom analytics logic on detected objects and metadata.
gvaattachroi	Adds user-defined regions of interest to perform inference on, instead of full frame.
gvaaudiodetect	Performs audio event detection using AclNet model.
gvaaudiotranscribe	Performs audio transcription using OpenVino GenAI Whisper model.
gvaclassify	Performs object classification. Accepts the ROI as an input and outputs classification results with the ROI metadata.
gvadetect	Performs object detection on a full-frame or region of interest (ROI) using object detection models such as YOLOv4-v11, MobileNet SSD, Faster-RCNN etc. Outputs the ROI for detected objects.
gvafpscounter	Measures frames per second across multiple streams in a single process.
gvafpsthrottle	Throttles the frame rate of a pipeline to a specified FPS value.
gvagenai	Performs inference with Vision Language Models using OpenVINO™ GenAI, accepts video and text prompt as an input, and outputs text description. It can be used to generate text summarization from video.
gvainference	Runs deep learning inference on a full-frame or ROI using any model with an RGB or BGR input.
gvametaaggregate	Aggregates inference results from multiple pipeline branches
gvametaconvert	Converts the metadata structure to the JSON format.
gvametapublish	Publishes the JSON metadata to MQTT or Kafka message brokers or files.
gvamotiondetect	Performs lightweight motion detection on NV12 video frames and emits motion regions of interest (ROIs) as analytics metadata.
gvapython	Provides a callback to execute user-defined Python functions on every frame. Can be used for metadata conversion, inference post-processing, and other tasks.
gvarealsense	Provides integration with Intel RealSense cameras, enabling video and depth stream capture for use in GStreamer pipelines.
gvastreammux	Multiplexes multiple input streams into a single pipeline with batch metadata support.
gvatrack	Performs object tracking using zero-term, or imageless tracking algorithms. Assigns unique object IDs to the tracked objects.
gvawatermark	Overlays the metadata on the video frame to visualize the inference results.
g3dinference	Performs deep learning inference on 3D LiDAR sensor data.
g3dlidarparse	Parses 3D LiDAR data for use in analytics pipelines.
g3dradarprocess	Processes 3D radar data for use in analytics pipelines.

For the details on supported platforms, please refer to System Requirements.

For installing Pipeline Framework with the prebuilt binaries or Docker* or to build the binaries from the open source, refer to Intel® DL Streamer Pipeline Framework installation guide.

New in 2026.1 Release#

Feature	Description
DLStreamer Coding Agent	AI-powered coding assistant that translates natural-language pipeline descriptions into working DL Streamer applications (Python or GStreamer CLI). Ships with a structured skill comprising a requirements questionnaire, model preparation guide, pipeline construction rules, design patterns, sample index, and debugging hints. Seven example prompts included: pose estimation, people detection & tracking, VLM safety compliance checks, event-based smart NVR, license-plate recognition, multi-stream compose, and DeepStream-to-DLStreamer Python conversion.
Upstream GstAnalytics API migration	Removed custom gstanalyticsgroupmtd and gstanalyticskeypointmtd implementations, migrating to upstream GStreamer 1.28+ API.
Full-frame GstAnalytics support	Added full-frame GstAnalytics support so inference results without a parent object detection are properly stored in GstAnalyticsRelationMeta and serialized to JSON. Includes frame-level classification and keypoint conversion.
Keypoints GstAnalytics metadata	Switched keypoints to GstAnalytics metadata API for standardized metadata handling.
Analytics element (gvaanalytics)	New element providing a framework for custom analytics logic on detected objects and metadata.
Stream multiplexer element (gvastreammux)	New element for multiplexing multiple input streams into a single pipeline with GstAnalyticsBatchMeta support.
3D inference element (g3dinference)	New element for performing deep learning inference on 3D LiDAR sensor data.
YOLO classification support	Added YOLO classification model support for image classification tasks.
PaddleOCRv5 model support	Added support for PaddleOCRv5 models for character recognition.
GETI semantic segmentation support	Added support for GETI semantic segmentation models.
CPU thread affinity parameter	Added affinity CPU thread affinity parameter to gvadetect/gvaclassify for performance tuning.
Skip raw tensors property	Added skip-raw-tensors property to gvaclassify and related components to reduce metadata overhead.
DepthConverter and pre-processing	Added DepthConverter and enhanced pre-processing capabilities for depth data.
New D3D11 backend for inference elements	New D3D11 GPU processing backend for inference elements including model sharing support.
Windows installer	Added Windows installer for simplified DL Streamer deployment.
Windows samples expansion	Added new Windows samples including YOLO, metapublish Kafka/MQTT.
Dynamic ONVIF camera discovery sample	New sample for dynamic ONVIF camera discovery with DLS pipeline startup.
gvaanalytics sample	New sample demonstrating the gvaanalytics element usage.
Inference performance optimization sample	New sample app demonstrating inference performance optimizations DLS can provide.
Custom frame selection for VLM sample	New sample pipeline with custom frame selection logic for VLM queries.
DLStreamer ONVIF Python wheel	Introduced dlstreamer.onvif package as an installable Python wheel (whl file).
PyTorch Image Models (TIMM) import	Added PyTorch Image Models (TIMM) model import script for model onboarding.
Python environment setup script	Added Python environment setup script for streamlined development setup.

Updated in 2026.1 Release#

Feature	Description
gvawatermark enhancements	Custom full-frame text support, dynamic Gaussian blur kernel sized to ROI, metadata-controlled watermark drawing, explicit enable-blur flag documentation.
DLS Optimizer enhancements	Time reduction, ability to pause/resume progress, retrieve partial results, output results to file, cross-stream grouping for batches and nireqs, pipeline validation based on counted detections.
Deep SORT tracker refactoring	Refactored Deep SORT Tracker with enhanced detection and track structures for improved tracking accuracy.
metaaggregate improvements	Fixed id=0 hash lookup with g_hash_table_lookup_extended, fixed shared group member duplication, and added generic semantic_tag copy for all mtd types.
gvaattachroi GstAnalytics dual-write	Full-frame keypoints attached by gvaattachroi now also write GstAnalytics metadata for visibility to gvawatermark.
jsonconverter enhancements	Added frame-level keypoint group conversion, frame-level classification via semantic_tag keys, and per-keypoint semantic_tag to JSON output.
ROI label handling	Enhanced ROI label handling: append label regardless of confidence and show confidence only if valid.
YOLOv10/YOLOv26 converters	Updated YOLOv10 and YOLOv26 converters to enforce NMS for improved detection results.
OpenVINO update	Update to OpenVINO 2026.1.
GStreamer update	Update to GStreamer 1.28.2 with GStreamer 1.28 build support. Removed GStreamer 1.26 support.
OpenCV update	Update to OpenCV 4.13.0.
NPU driver update	Update to NPU driver version 1.32.1.
GPU driver update	Updated GPU drivers to latest version.
Ubuntu update	Bump Ubuntu to 24.04.4 and kernel 6.17.
LidarMeta refactoring	Refactored LidarMeta implementation from C++ to C for improved portability.
Model downloading scripts	Improved Ultralytics and HuggingFace model downloading scripts with better error handling. Windows YOLO script now supports YOLOv26.
gvapython samples conversion	Converted gvapython samples to standalone Python GStreamer elements.
gvametaconvert documentation	Added missing documentation about add-rtp-timestamp property.
gvametapublish Windows support	Enabled gvametapublishkafka and gvametapublishmqtt on Windows.
CI infrastructure	Switched to self-hosted Kubernetes infrastructure, enabled configurable tests in Makefile, added Fedora 41 image building, modified PR workflow to save CI resources.
Windows build improvements	Build gstanalytics library from source, PDB file generation, fixed environment variable paths.
Python samples overview	Updated Python samples overview and READMEs.
Tests	Expanded optimizer unit tests, added GStreamer check framework unit tests for gvawatermark, run optimizer tests always. Added 41 unit tests for metaaggregate copy functions, extended tensor_convert_test with full-frame classification and keypoint round-trip tests.

Fixed in 2026.1#

#	Description
1	Fixed downloading of pallet defect detection model.
2	Fixed codeQL scan issues.
3	Fixed Coverity data race warnings in gvastreammux/demux.
4	Fixed GStreamer cache cleaning by prepending directories to PATH.
5	Fixed draw_face_attributes sample failing to build on Ubuntu 24 deb install due to missing OpenCV paths.
6	Fixed instant EOS and tracking metadata copy failure in multi-branch pipelines.
7	Fixed detections with 0 height/width causing issues.
8	Fixed DLSPS building errors.
9	Fixed path for downloading Windows assets.
10	Fixed Windows set environment path issue.
11	Fixed GIR to typelib compilation for Windows.
12	Fixed WinGet PowerShell module crash.
13	Fixed mapping of VA memory in gvawatermark.
14	Fixed incorrect default devices being used in optimizer.
15	Fixed images not showing in docs website.
16	Fixed analytics metadata documentation.
17	Fixed linking dlstreamer to home directory.
18	Fixed triggering CI when pushed to main.
19	Fixed documentation for DL Streamer and DeepStream coexistence.
20	Fixed queue name check and removed mutex unlock (reverted).
21	Fixed yamllint issues.
22	Removed deprecated ffmpeg_openvino and decode_resize_inference samples.

Known Issues - 2026.1#

Issue	Description
Preview Architecture 2.0 Samples	Preview Arch 2.0 samples have known issues with inference results.

Legacy Features and Deprecation Timeline - 2026.1#

List of the features and components to be deprecated in the future.

Feature	Target	Replacement
OMZ models	end of Q3’2026	Use HuggingFace, Ultralytics, TIMM
Model-proc-file	end of Q3’2026	ModelAPI (common with Geti)
gvapython element	end of Q4’2026	usage of regular GStreamer Python bindings
GstVideoRegionOfInterest meta + Intel extensions	end of Q4’2026	usage of GstAnalyticsMtd
Architecture 2.0 elements	end of Q4’2026	Preserve essential components and ideas
Alchemist GPU support	end of Q3’2026	Battlemage discrete GPUs
Tiger Lake support	end of Q4’2026	new supported units: Arrow Lake and Panther Lake
Ubuntu 22.04	end of Q4’2026	newer Ubuntu versions, including Ubuntu 26

Legal Information - 2026.1#

• GStreamer is an open source framework licensed under LGPL. See https://gstreamer.freedesktop.org/documentation/frequently-asked-questions/licensing.html. You are solely responsible for determining if your use of GStreamer requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of GStreamer.

Release Notes: Deep Learning Streamer (DL Streamer) Pipeline Framework Release 2026.0#

Version 2026.0#

Key highlights:#

New elements: gvafpsthrottle, g3dradarprocess, g3dlidarparse
New model support: YOLOv26, YOLO-E, RT-DETR, HuggingFace ViT
Streamlined integration with Ultralytics and HuggingFace model hubs
GstAnalytics metadata support: DLStreamer supports GstAnalytics metadata for object detection, classification, tracking and adds custom GstAnalytics extension for keypoints
gvawatermark overhaul: object bluring, text backgrounds, label filtering, extra fonts, thickness/color options, FPS overlay
Inference enhancements: batch timeout, OpenCV tensor compression for all devices
Windows platform: GPU inference via D3D11, gvapython support, CI integration, build/setup improvements
New Python samples: VLM Alerts, Smart NVR, ONVIF Discovery, face detection/age classification, open-vocabulary detection, RealSense, DL Streamer + DeepStream
Optimizer: multi-stream optimization, cross-stream batching, device selection, refactored with tests
Component updates: OpenVINO 2026.0.0, NPU driver 1.30, RealSense SDK 2.57.5
Library consolidation: merged gvawatermark3d, gvadeskew, gvamotiondetect, gvagenai into gstvideoanalytics
CI: Zizmor security scanning, Windows CI, Docker image size checks

Deep Learning Streamer (DL Streamer) Pipeline Framework is a streaming media analytics framework, based on GStreamer* multimedia framework, for creating complex media analytics pipelines. It ensures pipeline interoperability and provides optimized media, and inference operations using Intel® Distribution of OpenVINO™ Toolkit Inference Engine backend, across Intel® architecture, CPU, discrete GPU, integrated GPU and NPU. The complete solution leverages:

Open source GStreamer* framework for pipeline management
GStreamer* plugins for input and output such as media files and real-time streaming from camera or network
Video decode and encode plugins, either CPU optimized plugins or GPU-accelerated plugins based on VAAPI
Deep Learning models converted from training frameworks TensorFlow*, Caffe* etc.

The following elements in the Pipeline Framework repository:

Element	Description
gvaattachroi	Adds user-defined regions of interest to perform inference on, instead of full frame.
gvaaudiodetect	Performs audio event detection using AclNet model.
gvaaudiotranscribe	Performs audio transcription using OpenVino GenAI Whisper model.
gvaclassify	Performs object classification. Accepts the ROI as an input and outputs classification results with the ROI metadata.
gvadetect	Performs object detection on a full-frame or region of interest (ROI) using object detection models such as YOLOv4-v11, MobileNet SSD, Faster-RCNN etc. Outputs the ROI for detected objects.
gvafpscounter	Measures frames per second across multiple streams in a single process.
gvafpsthrottle	Throttles the frame rate of a pipeline to a specified FPS value.
gvagenai	Performs inference with Vision Language Models using OpenVINO™ GenAI, accepts video and text prompt as an input, and outputs text description. It can be used to generate text summarization from video.
gvainference	Runs deep learning inference on a full-frame or ROI using any model with an RGB or BGR input.
gvametaaggregate	Aggregates inference results from multiple pipeline branches
gvametaconvert	Converts the metadata structure to the JSON format.
gvametapublish	Publishes the JSON metadata to MQTT or Kafka message brokers or files.
gvamotiondetect	Performs lightweight motion detection on NV12 video frames and emits motion regions of interest (ROIs) as analytics metadata.
gvapython	Provides a callback to execute user-defined Python functions on every frame. Can be used for metadata conversion, inference post-processing, and other tasks.
gvarealsense	Provides integration with Intel RealSense cameras, enabling video and depth stream capture for use in GStreamer pipelines.
gvatrack	Performs object tracking using zero-term, or imageless tracking algorithms. Assigns unique object IDs to the tracked objects.
gvawatermark	Overlays the metadata on the video frame to visualize the inference results.
g3dradarprocess	Processes 3D radar data for use in analytics pipelines.
g3dlidarparse	Parses 3D lidar data for use in analytics pipelines.

For the details on supported platforms, please refer to System Requirements. For installing Pipeline Framework with the prebuilt binaries or Docker* or to build the binaries from the open source, refer to DL Streamer Pipeline Framework installation guide.

New in this Release

Title	High-level description
3D elements (g3dradarprocess, g3dlidarparse)	New 3D plugin support with g3dradarprocess element for radar data processing and g3dlidarparse element for lidar data parsing, enabling 3D analytics pipelines.
FPS throttle element (gvafpsthrottle)	New element to throttle the frame rate of a pipeline to a specified FPS value.
YOLOv26 model support	Added converters and post-processing for YOLOv26 models, including oriented bounding box (OBB) support and INT8 GPU inference. Added YOLOv26 to supported models in samples.
RT-DETR model support	Added RT-DETR support implementation for real-time detection transformer models.
HuggingFace ViT classifier support	Added HuggingFace Vision Transformer (ViT) classifier config parser for inference.
Batch timeout for inference elements	Added batch-timeout parameter to inference elements, allowing control over batching wait time.
VLM Alerts sample	New Python sample for VLM-based alerts with displaying results on produced video.
Smart NVR sample	New Python sample for Smart NVR with added custom analytics logic (gvaAnalytics) and custom storage (gvaRecorder) elements.
ONVIF Camera Discovery sample	New Python sample demonstrating ONVIF camera discovery and DL Streamer pipeline launcher.
Face detection & age classification sample	New Python sample for face detection and age classification using HuggingFace models.
Open-vocabulary object detection sample	New Python sample with open-vocabulary prompt for object detection.
DL Streamer + DeepStream coexistence sample	New sample demonstrating DL Streamer and DeepStream working in one system.
Motion detect sample (Windows)	New sample demonstrating DL Streamer gvamotiondetect functionality
RealSense element usage sample	New sample demonstrating gvarealsense element usage.

Updated in this Release

Title	High-level description
gvawatermark enhancements	Major enhancements to the gvawatermark element: display configuration options (thickness, color index), text background support, inclusive/exclusive label filtering, additional font support, average FPS info overlay, and visual documentation.
DLS Optimizer enhancements	Optimizer refactored with multi-stream optimization, cross-stream batching, improved FPS reporting, and device selection improvements.
gvametaconvert enhancements	Added reference NTP timestamp from RTCP sender meta extraction to gvametaconvert element
ROI object construction enhacement	For existing GstAnalyticsODMtd only, creates GstVideoRegionOfInterestMeta until full GstAnalytics migration
Latency tracer multi-source/sink support	Extended latency_tracer to support multiple sources and multiple sinks.
Detection anomaly converter	Refactored and enhanced anomaly logic in DetectionAnomalyConverter.
FP32 precision in BoxesLabelsConverter	Added FP32 precision support in BoxesLabelsConverter label parsing.
Bounding box validation	Added extra validation of bounding boxes to improve robustness.
OpenCV tensor compression for all devices	Use OpenCV tensor compression for all inference devices, yielding best performance across CPU/GPU/NPU.
Model API refactoring	Moved Model API parser to separate files; added conversion from Ultralytics and HuggingFace metadata to Model API.
Python samples overview	Added overview section for Python samples; updated READMEs.
Tests	Expanded coverage of functional and unit tests.
Windows: GPU inference with D3D11	Added support for GPU inference on Windows using D3D11.
Windows: gvapython support	Added Windows support for gvapython element and gstgva Python bindings.
Windows: enhanced build & setup	Enhanced Windows build/setup scripts, added remove script, Visual C++ runtime handling, and JSON output for Windows samples.
Windows: CI integration	Enabled Windows tests in GitHub Actions workflow, model downloads on Windows.
Library consolidation	Merged gvawatermark3d, gvadeskew, gvamotiondetect, and gvagenai into the gstvideoanalytics library.
OpenVINO update	Update to OpenVINO 2026.0.0.
NPU driver update	Update to NPU driver version 1.30.
RealSense update	Update to Intel RealSense SDK 2.57.5.
Model download script improvements	Simplified YOLO model download script, enhanced INT8 quantization, refactored YOLOv8+ export/quantize, added model validation.
CI: Zizmor security scanning	Added Zizmor GitHub Actions security scanner.

Fixed

#	Issue Description
1	Fixed YOLO26 model inference on GPU FP16/FP32.
2	Fixed threshold parameter in gvadetect not working with PDD model.
3	Fixed yolov8-seg inference result different from OpenVINO.
4	Fixed gvapython failing to read yolo-pose keypoint metadata.
5	Fixed NV12 frame data in Python by removing padding correctly.
6	Fixed watermark default text background behaviour.
7	Fixed check for pad_value in model XML file.
8	Fixed yolo_v10.cpp compile error on Windows.
9	Fixed DLL output paths on Windows.
10	Fixed compilation warnings on Windows.
11	Fixed timestamp on VS 2026.
12	Fixed GStreamer downloader by adding UserAgent.
13	Fixed libva path setup in setup_dls_env.ps1
14	Removed libva dependency for monolithic elements on Windows.
15	Fixed latency tracker for smart intersection pipelines.
16	Fixed environment variable paths in Ubuntu install guide.
17	Fixed directory already exists error during build.
18	Removed duplicate gvametapublish element register.
19	Reverted RTP timestamp feature due to issues.
20	Fixed download public models script - versions of NumPy, Onnx, and Seaborn.
21	Fixed missing context in Build Docker instruction.
22	Fixed formatting in installation guide and developer guide documentation.

Known Issues

Issue	Issue Description
Preview Architecture 2.0 Samples	Preview Arch 2.0 samples have known issues with inference results.

Legacy Features and Deprecation Timeline#