Detection and Tracking
Object detection is a technique that identifies and locates objects in a scene. This enables you to detect 3-D objects in a point cloud. Lidar Toolbox™ includes functionality that enables you to detect objects using geometric shape fitting or deep learning with convolutional neural networks.
Geometric shape fitting — Detect the 3-D geometry of the objects in the point cloud by using ground segmentation and plane-fitting algorithms. You can detect the location, dimensions and direction of each object. You can use the detected objects for downstream workflows such as tracking, path planning and labeling.
Deep learning — A deep learning approach to object detection uses convolutional neural networks to perform object detection. Lidar Toolbox includes object detection workflows that use neural networks such as PointPillars and Complex-YOLO v4. You can train a custom object detection model, or use the available pretrained networks and further tune it for your application. The toolbox also supports CUDA® MEX code generation for PointPillars and SqueezeSegV2 networks.
Object tracking is a technique that estimates and tracks the movement of objects across multiple scans of a scene. Object tracking consists of assigning a unique ID to detected objects and tracking their movement across point cloud frames. Lidar Toolbox includes detection and tracking workflows for vehicles, road lanes, and curbs. Most of these workflows use the joint probabilistic data association (JPDA) tracker.
|Fit cuboid over point cloud|
|Fit plane to 3-D point cloud|
|Object for storing parametric plane model|
|Parametric cuboid model|
Load Training Data
|Ground truth label data|
|Combine data from multiple datastores|
|Datastore with custom file reader|
|Datastore for bounding box label data|
Augment and Preprocess Training Data
|Create randomized 3-D affine transformation|
|Apply geometric transformation to bounding boxes|
|Transform 3-D point cloud|
|PointPillars object detector|
|Train PointPillars object detector|
|Detect objects using PointPillars object detector|
|Detect LOAM feature points from 3-D lidar data|
|Create training data for lidar object detection|
|Evaluate average orientation similarity metric for object detection|
|Compute bounding box overlap ratio|
- Deep Learning with Point Clouds
Learn point cloud processing using deep learning.
- Getting Started with PointPillars
Define PointPillars network and learn how to perform object detection using the same.
- Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
- List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB®.