Single Stream Pipeline

This section describes model inference for a single video stream.
Although the example pipeline performs inference using the YOLOv5s model, it can be easily adapted to various vision tasks with different models. The pipeline below reads video frames from a file and performs a pre-infer-post process tailored to YOLOv5s.

The inference results are then drawn using dxosd, and the final output is displayed via a display sink.

The pipeline in the figure is defined in dx_stream/pipelines/single_network/object_detection/run_YoloV5S_PPU.sh and can be used as a reference for execution.

When performing object detection on a single stream input, multi-object tracking can be enabled by adding dxtracker. As shown below, by inserting the tracker after the YOLO object detection, detected objects can be tracked over time.

The pipeline in the figure is defined in dx_stream/pipelines/tracking/run_multi_object_tracker.sh and can be used as a reference for execution.

Explanation

Element Descriptions

• urisourcebin: Specifies the input video file. The uri property must be set to the file path of the video you wish to process.
• decodebin: Decodes the input video stream.
• dxpreprocess: Applies pre-processing according to the configuration file specified in the config-file-path.
• dxinfer: Runs inference using the YOLOv5s model. The model configuration file path is specified in config-file-path.
• dxpostprocess: Post-processes the model's output tensor to extract metadata. The configuration file path is specified in config-file-path.
• dxtracker: Tracks objects detected by the YOLO model using the OC-SORT algorithm.
• dxosd: Draws object detection results (e.g., bounding boxes, class labels, etc.) on the video frames.
• fpsdisplaysink: Displays the video frames along with the FPS (frames per second) information. The sync=false property ensures that all frames are displayed without being dropped.

Usage Notes

Custom Models

This pipeline is not limited to the YOLOv5s model. It can be easily adapted for other AI tasks by updating the corresponding model and configuration files.

• Classification: Image classification tasks
• Segmentation: Pixel-wise semantic segmentation
• Pose Estimation: Detecting human keypoints

Update the config-file-path property in the dxpreprocess, dxinfer, and dxpostprocess elements to match your model’s configuration.

Pipeline Behavior

This pipeline runs synchronously because it does not include queue elements. Each element waits for the previous one to finish processing before continuing. This simplifies data flow but may affect performance on multi-core systems.

Sink Element Options

You can replace fpsdisplaysink with other display options.

• ximagesink: Displays video in an X11 window environment
• autovideosink: Automatically selects the most suitable video sink for the platform

Object Detection Requirement

• The dxtracker element requires bounding box information. Therefore, object detection must precede tracking in the pipeline.

Visualization

• The dxosd element overlays both detection and tracking results. Each object is assigned a unique track ID by the dxtracker element, which is visualized along with the bounding box.

Buffer Handling

• In this example, fpsdisplaysink is set with sync=false, which means no frames are -dropped—all frames are displayed as-is.
• If you want to sync playback with the original video frame rate or reduce latency, set sync=true.

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