# Qualcomm IM SDK release 1.6 Tab Qualcomm Linux Tab Ubuntu - *class* tabincludedirective - ## Release information Table : Software version | Software | Version | | --- | --- | | Yocto | Scarthgap 5.0.11 | | Kernel | 6.6.97 | Table : Release tag version | Release tag | Version | | --- | --- | | Firmware | r1.0\_00106.0 | | Manifest | qcom-6.6.97-QLI.1.6-Ver.1.2 | | Meta-qcom-extras | r1.0\_00107.0 | | Qualcomm IM SDK | qcom-6.6.97-QLI.1.6-Ver.1.2\_qim-product-sdk-2.1.1 | Table : Supported platforms and reference kits | SoC platforms | Reference kits | | --- | --- | | QCS6490 | | | QCS5430 | | | Qualcomm Dragonwing^TM^ IQ-9075 | | | Qualcomm Dragonwing^TM^ IQ-8275 | Qualcomm Dragonwing^TM^ IQ-8275 Beta Evaluation Kit (EVK) | ## Contents of the release The contents of the Qualcomm IM SDK release include: - Recipes for building the individual components: - Qualcomm^®^ Intelligent Multimedia SDK (IM SDK) - Lite Runtime (LiteRT) - Qualcomm^®^ Neural Processing SDK - Qualcomm^®^ AI Engine direct SDK - Sample applications that demonstrate how to use the Qualcomm IM SDK to develop AI edge-based applications. To get started with the Qualcomm IM SDK, see [Qualcomm Intelligent Multimedia (IM SDK) Quick Start Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-51/qmi-sdk-qsg-landing-page.html). ## New features The following new features applicable to QCS6490, Dragonwing IQ-9075, and Dragonwing IQ-8275 are introduced in the Qualcomm IM SDK release: - `qtimlpostprocess` is a new consolidated plugin that provides a modular C++ API for all postprocessing tasks. This unified design simplifies integration and makes it easier to add support for new modules. The plugin supports the postprocessing for the following types of modules: > > > - Audio classification (`qtimlaclassification`) > - Image classification (`qtimlvclassification`) > - Image segmentation (`qtimlvsegmentation`) > - Object detection (`qtimlvdetection`) > - Pose estimation (`qtimlvpose`) > - Super resolution (`qtimlvsuperresolution`) Note The previously used postprocessing plugins will be deprecated in future. Ensure to migrate to `qtimlpostprocess` for future compatibility. For information about the `qtimlpostprocess` plugin, see [Migration Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-150/introduction.html#changes-in-qualcomm-im-sdk). - The tripwire people counting heatmap service provides advanced people analytics by generating spatial heatmaps that visualize crowd density and movement patterns across defined zones within the camera’s field of view. > > > - It supports both real-time and historical data queries using a 64×64 array. > - Alert lifecycle management is enhanced through start and end time tracking, with person re-identification (re-ID) ensuring persistent tracking and accurate alert clearance. - The vehicle detection and alerting service analyzes vehicle behavior using AI-driven detection and configurable alerts. > > > - It supports creating and deleting custom polygonal regions within the camera’s field of view for precise spatial targeting. > - You can query analytics and set triggers for either the full-screen or specific regions based on dwell time or occupancy thresholds. > - Additional capabilities include vehicle counting, 64×64 heatmap generation, loitering detection, and alert lifecycle tracking using start and end timestamps with vehicle re-identification. > - Basic vehicle attributes are detected, with future support planned for expanded classification. - End-to-end support for sigLIP model, [Segformer-Base - Qualcomm AI Hub](https://aihub.qualcomm.com/iot/models/segformer_base?searchTerm=segf) and 20+ models from Qualcomm AI Hub. - End-to-end support for multiframe video classification using [Video-MAE - Qualcomm AI Hub](https://aihub.qualcomm.com/iot/models/video_mae?searchTerm=MAE). ## Sample applications | Sample application | Description | Supported SoCs | | --- | --- | --- | | **AI/ML applications** | **AI/ML applications** | **AI/ML applications** | | `gst-ai-event-encoder` | Application receives the live video stream input from camera, file, or RTSP source. When a human enters the video frame the application preprocesses the video, runs inferences on the AI hardware, and encodes the video. The encoding stops 5 seconds after the human moves away from the frame and restarts when anyone enters the frame. | QCS6490, Dragonwing IQ-9075, and Dragonwing IQ-8275 | | **Video application** | **Video application** | **Video application** | | `gst-rtmp-stream-example` | Camera feed from ISP and RTSP sources is converted to RTMP stream. The sample application is used in security systems and media broadcasting. | QCS6490, Dragonwing IQ-9075, and Dragonwing IQ-8275 | For the complete list of sample applications supported in the Qualcomm IM SDK and instructions on how to run them, see [Sample applications](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-50/example-applications.html). ## Issues resolved The following issues are resolved in the Qualcomm IM SDK release: - Stability issues are observed with GStreamer pipeline in batched model use cases. - For QCS6490: > > > - Performance drops in superresolution end-to-end use cases due to postprocessing issues affecting GPU stability. - While running the `gst-daisychain-detection-pose` sample application, the output doesn’t display pose information within the extracted ROI regions. - The following applications have stability issues due to bugs in the application: - `gst-camera-three-stream-encode-file-detection-display-classification-rtsp.py` - `gst-camera-two-stream-detection-and-classification-side-by-side.py` - Use cases involving the `qtismartvencbin` plugin fail due to a missing versioned library. - Starting from Qualcomm Neural Processing SDK version 2.34, LiteRT models encounter failure when executed with the benchmark\_model application using the Qualcomm Neural Network external delegate. This issue is resolved from the Qualcomm Neural Processing SDK version 2.37. ## Limitations The following are the known limitations in the Qualcomm IM SDK release: - When using the `qtioverlay` plugin with detection models, frame drops may occur, especially with many detections. **Workaround**: Use `qtivcomposer` for detection-based ML use cases. - AI/ML parallel inference for 32 streams varies between 20–30 fps depending on the model and input stream, which is less than the expected 30 fps. - Segfault occurs while using Ctrl + C for Qualcomm^®^ Neural Processing use cases with DLC models. - Frame drops are observed with the Qualcomm^®^ Neural Network plugin while running on GPU delegate. - Low FPS is observed with GStreamer pipelines for daisychain and pose detection. - High inference time is observed with `deeplabv3_resnet50.dlc`. - Concurrent streams with smart codec functionality encounter pipeline stalls at the end of the stream. - Reverse playback is limited to video streams with a GOP length that fits within the buffer limitations of the video driver (<= 26). - For Dragonwing IQ-9075 and Dragonwing IQ-8275: > > > - Random corruption is observed at the beginning of playback for low-resolution (480p) NV12 UBWC compressed streams. > - Stability issues are observed while running multibatch ML use cases. - The `waylandsink` plugin doesn’t support displaying windows at custom screen coordinates. - Support for opencv isn’t enabled inside the Qualcomm IM SDK docker container. - Use cases with files having dynamic resolution change (DRC) may fail. This issue occurs because the video driver allocates buffers that are only partially accessible to the GPU driver. - When using the `qtimlpostprocess` plugin for postprocessing, a display latency occurs after approximately 30 minutes of running ML use cases on a camera stream. - Camera streams with lens distortion correction (LDC) or electronic image stabilization (EIS) enabled may experience stability issues due to an underlying issue within the camera userspace library. - The camera Perport feature (for multicamera aggregation) may experience stability issues in back-to-back iterations due to an underlying issue within the camera userspace library. - *class* tabincludedirective - ## Release information Table : Software version | Software | Version | | --- | --- | | Ubuntu | Noble 24.04 | | Kernel | 6.8.0 | Table : Supported platforms and reference kits | SoC platforms | Reference kits | | --- | --- | | QCS6490 | | | QCS5430 | | | Qualcomm Dragonwing^TM^ IQ-9075 | | ## Contents of the release The contents of the Qualcomm IM SDK release include: - Recipes for building the individual components: - Qualcomm IM SDK - Lite Runtime (LiteRT) Note LiteRT was formerly known as TensorFlow Lite. - Qualcomm Neural Processing SDK - Qualcomm AI Engine direct SDK - Sample applications that demonstrate how to use the Qualcomm IM SDK to develop AI edge-based applications. To get started with the Qualcomm IM SDK, see [Qualcomm Intelligent Multimedia (IM SDK) Quick Start Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-51/qmi-sdk-qsg-landing-page.html). ## New features The following new features are introduced in the Qualcomm IM SDK release: - Microservices: > > > - Yolo V8-based person detection. > - Face detection or recognition for access control. > - General purpose daisy chain. - AI-driven safety and monitoring system. > > > - **Restricted zone alerts**: Define zones in the camera’s FOV to generate alerts when someone enters these areas. > - **PPE compliance**: Ensure safety gear compliance with alerts for missing or restricted items, configurable using a web interface. - The GStreamer version has been upgraded to 1.24. - Qualcomm plugins utilize a new color format, NV12\_Q08C, which is introduced for NV12 with UBWC. This color format replaces the previous method of using flags such as compression=ubwc. - V4L2 supports `dmabuf` and `dmabuf-import` modes for efficient zero-copy data transfer. - Audio AI: End-to-end support for general audio classification including sampling, preprocessing, inference, and post-processing. - The docker base image is updated to Ubuntu 24.04 and the GStreamer version within the docker environment is 1.24. ## Sample applications | Sample application | Description | Supported SoC | | --- | --- | --- | | **AI/ML applications** | **AI/ML applications** | **AI/ML applications** | | `gst-ai-audio-classification` | Classification on streams from audio source. | QCS6490 | | `gst-ai-metadata-parser-example` | Parses metadata using appsink plugin on streams from camera, file, or RTSP sources. Also, it provides the human count from the stream. | QCS6490 | | `gst-ai-usb-camera-app` | USB single camera streaming for preview, video encoder, or network (RTSP) along with object detection and preview. | QCS6490 | | **Video application** | **Video application** | **Video application** | | `gst-opencv-transform` | Video playback using opencv API. Parses input video file, captures frame using cv video capture, and streams videoplay and composites to display using waylandsink. | QCS6490 | | `gst-jpg-image-decode` | Decodes JPEG images where user can view decoded images on waylandsink. | QCS6490 | | `gst-camera-opencv-resize` | Live camera stream using opencv API, which includes color conversion and resize. The application captures camera frame as an input using cv video capture, converts to RGBA using downstream plugin, and resizes to user–specified resolution. Resize output displays on screen. | QCS6490 | | `gst-jpg-decode-example` | Collects the live video input from a camera, file, or an RTSP stream and uses the Qualcomm Neural Network face detection model to produce a preview with the overlaid AI model output on the HDMI display. | QCS6490 | Note `gst-usb-camera-example` is removed from multimedia sample applications. Use `gst-ai-usb-camera-app` instead of `gst-usb-camera-example`. For the complete list of sample applications supported in the Qualcomm IM SDK and instructions on how to run them, see [Sample applications](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-50/example-applications.html). ## Issues resolved The following issues are resolved in the Qualcomm IM SDK release: - A drop of 1 to 2 fps may be observed with the three-stream camera use case. - Concurrent streams with smart codec functionality encounter cleanup errors at end of stream. - Pose estimation for the `facemap_3dmm_quantized.bin` (Qualcomm Neural Network) AI hub model doesn’t work with the Qualcomm IM SDK pipeline due to caps mismatch. - Caps mismatch with the AI hub face detection Qualcomm Neural Network model. - Support for the FastCV engine is disabled in qtivtransform. ## Limitations The following are the known limitations in the Qualcomm IM SDK release: - Ubuntu desktop doesn’t support sample applications and AI Workflow. - Qualcomm Neural Network use cases don’t work with the `libqnn1` default package. **Workaround**: Upgrade to Qualcomm Neural Network 2.36 (default package is Qualcomm Neural Network 2.35). - The device stops responding when using super resolution use cases on the x04.1 build. - The WebRTC use cases aren’t enabled on this release. - FPS drops to zero while testing multistream AI Camera use cases for long durations. - The following sample applications fail: - `gst-video-playback-example` - `gst-opencv-transform.py` - `gst-jpg-image-decode.py` - `gst-camera-opencv-resize.py` - Camera pipeline hangs while quitting the pipeline of various multistream encode use cases. - The `gst-ai-audio-classification` sample application fails with GPU delegate and displays the error message. > > > ERROR Failed to parse zone configuration > Copy to clipboard ## Related documents - [Qualcomm Intelligent Multimedia SDK (IM SDK) Reference](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-50/qimsdk_landing_page.html) - [Qualcomm Intelligent Multimedia (IM SDK) Quick Start Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-51/qmi-sdk-qsg-landing-page.html) - [Qualcomm Dragonwing RB3 Gen 2 Development Kit Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-251/landing-page.html) - [Qualcomm Dragonwing IQ-9075 Evaluation Kit user guide – Linux](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-261/iq9-ug-landing-page.html) - [Qualcomm Linux Build Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-254/build_landing_page.html) - [Migration Guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70022-150/introduction.html) Last Published: May 14, 2026 [Previous Topic Qualcomm IM SDK release 1.7](https://docs.qualcomm.com/bundle/publicresource/80-80022-52/topics/qim-sdk-release-1-7.md) [Next Topic Qualcomm IM SDK release 1.5](https://docs.qualcomm.com/bundle/publicresource/80-80022-52/topics/qim-sdk-release-1-5.md) Source: [https://docs.qualcomm.com/doc/80-80022-52/topic/qim-sdk-release-1-6.html](https://docs.qualcomm.com/doc/80-80022-52/topic/qim-sdk-release-1-6.html)