> This page is for version 9.2 (default). > For other versions, use one of these documentation indexes: > - 9.2 (default): https://docs.nvidia.com/deepstream/9.2/llms.txt > - Archives: https://docs.nvidia.com/deepstream/archives/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/deepstream/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/deepstream/_mcp/server. # Gst-nvdsvisionencoder-c The Gst-nvdsvisionencoder-c plugin is a pure C/C++ GStreamer plugin that generates ReID (Re-Identification) embeddings for tracked objects using either Triton Inference Server or a local TensorRT engine, and attaches them to DeepStream metadata as `NVDS_TRACKER_OBJ_REID_META`. Compared to an equivalent Python plugin, it attaches metadata directly via C pointers (avoiding wrapper/binding issues), runs without Python overhead, and produces metadata that is correctly visible to all downstream components (for example `deepstream-test5-app`, Gst-nvmsgconv). It also supports embedding caching with optical-flow-based staleness prediction and an optional DLA-based appearance-change validator, both of which can skip full re-inference for objects whose appearance has not meaningfully changed. ## Inputs and Outputs * Inputs * GStreamer buffer containing NvBufSurface (NVMM) frames with tracked-object metadata * Control parameters * enable, backend, triton-url, triton-model, tensorrt-engine, onnx-model, batch-size, min-crop-size, gpu-id, device, skip-interval, embedding-classes, verbose * smart-infer, cache-refresh-interval, cache-max-size (embedding cache) * ofa-predict, ofa-motion-threshold, ofa-high-motion-threshold (optical-flow-based staleness prediction) * dla-validator, dla-validator-engine, dla-core, dla-change-threshold (DLA appearance-change validator) * Output * The same GStreamer buffer, with `NVDS_TRACKER_OBJ_REID_META` (ReID embeddings) attached to each tracked object's metadata ## Features The following table summarizes the features of the plugin. **Gst-nvdsvisionencoder-c plugin features** | Feature | Description | Release | | ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | | ReID embedding generation | Generates ReID embeddings for tracked objects via Triton Inference Server or a local TensorRT engine | DS 9.2 | | NVDS_TRACKER_OBJ_REID_META attachment | Attaches generated embeddings directly to DeepStream tracked-object metadata using native C pointers | DS 9.2 | | Minimum crop-size filtering | Skips embedding generation for objects smaller than min-crop-size | DS 9.2 | | Embedding caching | Caches per-object embeddings (smart-infer) and periodically re-infers them (cache-refresh-interval), avoiding redundant inference for already-embedded tracked objects | DS 9.2 | | Optical-flow staleness prediction | Uses hardware optical flow motion vectors (ofa-predict) to transform cached embeddings instead of re-inferring when object motion is below a configurable threshold | DS 9.2 | | DLA appearance-change validator | Gates full vision-encoder inference behind a lightweight DLA (or GPU fallback) appearance-change detector | DS 9.2 | ## Gst Properties The following table describes the Gst properties of the Gst-nvdsvisionencoder-c plugin. **Gst-nvdsvisionencoder-c plugin gst properties** | Property | Meaning | Type and Range | Example Notes | | ------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------- | -------------------------------------------- | | enable | Enables vision encoding | Boolean | enable=1 | | backend | Inference backend to use (triton or tensorrt) | String | backend=triton | | triton-url | Triton Inference Server URL | String | triton-url=localhost:8001 | | triton-model | Triton model name | String | triton-model=radio_clip_trt | | model-variant | Model variant (for compatibility) | String | model-variant=default | | tensorrt-engine | Path to a TensorRT engine (.plan) file; settable while PLAYING to reload a prebuilt engine on the fly, but the new engine must match the live one's input resolution, embedding dimension, and batch capacity | String | tensorrt-engine=/path/to/model.plan | | onnx-model | Path to an ONNX model file used to build the engine if it is not found; cold-start configuration only (setting it on a running element stores it for the next start rather than loading it -- use tensorrt-engine for a runtime update) | String | onnx-model=/path/to/model.onnx | | batch-size | TensorRT batch size for engine building/inference | Integer, 1 to 4,294,967,295 | batch-size=16 | | min-crop-size | Minimum crop size (width or height) below which an object is skipped | Integer, 8 to 512 | min-crop-size=32 | | verbose | Enables verbose logging | Boolean | verbose=0 | | gpu-id | GPU device ID | Integer, 0 to 7 | gpu-id=0 | | device | Device type used for inference (cuda or cpu) | String | device=cuda | | skip-interval | Frame skip interval (0 = none, 1 = every other frame, 2 = every 3rd frame, etc.) | Integer, 0 to 100 | skip-interval=0 | | embedding-classes | Semicolon-separated list of classes to generate embeddings for; empty means all classes | String | embedding-classes=Person;Vehicle | | query-only | Initializes the model for REST API query handling only and skips per-frame pipeline inference | Boolean | query-only=0 | | smart-infer | Skips inference for tracked objects whose embedding is already cached | Boolean | smart-infer=0 | | cache-refresh-interval | Re-infers cached objects every N frames; 0 means never re-infer while tracked | Integer, 0 to 10,000 | cache-refresh-interval=0 | | cache-max-size | Maximum number of object embeddings to cache; oldest entries are evicted first | Integer, 10 to 100,000 | cache-max-size=10000 | | ofa-predict | Uses hardware optical flow motion vectors to predict embedding staleness and transform cached embeddings instead of re-inferring | Boolean | ofa-predict=0 | | ofa-motion-threshold | Per-object average motion magnitude, in flow-vector units (\~pixels/frame), below which the cached embedding is trusted | Float, 0.0 to 100.0 | ofa-motion-threshold=5.0 | | ofa-high-motion-threshold | Per-object motion magnitude above which full re-inference is forced because the object's appearance likely changed significantly | Float, 0.0 to 500.0 | ofa-high-motion-threshold=20.0 | | dla-validator | Enables a lightweight appearance-change detector on DLA (Jetson) or GPU fallback to gate full vision-encoder inference | Boolean | dla-validator=0 | | dla-validator-engine | Path to the TensorRT engine for the lightweight appearance-change detector | String | dla-validator-engine=/path/to/validator.plan | | dla-core | DLA core to use for the validator; -1 uses GPU fallback | Integer, -1 to 7 | dla-core=-1 | | dla-change-threshold | Probability threshold above which the validator considers the object's appearance changed | Float, 0.0 to 1.0 | dla-change-threshold=0.5 |