The Basic Object Detector and Tracker sample demonstrates how the DNN Interface can be used for object detection and the 2D object tracking capabilities of the BoxTracker module.
The sample streams a H.264 or RAW video and runs DNN inference on each frame to detect objects using NVIDIA® TensorRT™ model.
The interpretation of the output of a network depends on the network design. In this sample, two output blobs (with coverage
and bboxes
as blob names) are interpreted as coverage and bounding boxes.
For each frame, it detects the object locations and tracks the objects between video frames. Currently, the object tracker resorts to image feature detection and tracking. The tracker uses feature motion to predict the object location.
The Basic Object Detector and Tracker sample, sample_object_detector_tracker
, accepts the following optional parameters. If none are specified, it performs detections on a supplied pre-recorded video.
./sample_object_detector_tracker --input-type=[video|camera] --video=[path/to/video] --camera-type=[camera] --camera-group=[a|b|c|d] --camera-index=[0|1|2|3] --slave=[0|1] --tensorRT_model=[path/to/TensorRT/model]
Where:
--input-type=[video|camera] Defines if the input is from live camera or from a recorded video. Live camera is supported only on NVIDIA DRIVE platforms. Default value: video --video=[path/to/video] Specifies the absolute or relative path of a raw or h264 recording. Only applicable if --input-type=video Default value: path/to/data/samples/sfm/triangulation/video_0.h264. --camera-type=[camera] Specifies a supported camera gmsl type Only applicable if --input-type=camera. Default value: ar0231-rccb-bae-sf3324 --camera-group=[a|b|c|d] Specifies the group where the camera is connected to. Only applicable if --input-type=camera. Default value: a --camera-index=[0|1|2|3] Specifies the camera index on the given port. Default value: 0 --slave=[0|1] Setting this parameter to 1 when running the sample on SoC B allows to access a camera that is being used on SoC A. Only applicable if --input-type=camera. Default value: 0 --tensorRT_model=[path/to/TensorRT/model] Default value: path/to/data/samples/detector/<gpu-architecture>/tensorRT_model.bin, where <gpu-architecture> can be `ampere-discrete` or `ampere-integrated`.
data/samples/detector/ampere-discrete/tensorRT_model.bin.json
./sample_object_detector_tracker
The video file must be a H.264 or RAW stream. Video containers such as MP4, AVI, MKV, etc. are not supported.
./sample_object_detector_tracker \ --input-type=video \ --video=<video file.h264/raw> \ --tensorRT_model=<TensorRT model file>
./sample_object_detector_tracker \ --input-type=camera \ --camera-type=<rccb_camera_type> \ --camera-group=<camera group> \ --camera-index=<camera idx on camera group> \ --tensorRT_model=<TensorRT model file>
where <rccb_camera_type>
is a supported RCCB
sensor. See Cameras Supported for the list of supported cameras for each platform.
The sample creates a window, displays the video streams, and overlays the list of features and detected/tracked bounding boxes of the objects with IDs.
The color coding of the overlay is:
For more information, see: