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Detection and Tracking

Object detection, shape fitting, and tracking in lidar point cloud data

Object detection is a major lidar application. The objects detected in lidar point cloud data are crucial for downstream workflows like tracking and labeling. Lidar Toolbox™ provides the object detection CNN PointPillars for developing custom object detection models.

Lidar Toolbox provides detection and tracking workflows for vehicles and road lanes. Most of the tracking workflows use the joint probabilistic data association (JPDA) tracker.


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pcfitcuboidFit cuboid over point cloud
pcfitplaneFit plane to 3-D point cloud
pcnormalsEstimate normals for point cloud
planeModelObject for storing a parametric plane model
cuboidModelParametric cuboid model

Load Training Data

groundTruthGround truth label data
combineCombine data from multiple datastores
fileDatastoreDatastore with custom file reader
boxLabelDatastoreDatastore for bounding box label data

Augment and Preprocess Training Data

randomAffine3dCreate randomized 3-D affine transformation
bboxwarpApply geometric transformation to bounding boxes
pctransformTransform 3-D point cloud

Visualize Results

showShapeDisplay shapes on image, video, or point cloud
pcshowPlot 3-D point cloud

Evaluate Results

evaluateDetectionAOSEvaluate average orientation similarity metric for object detection
bboxOverlapRatioCompute bounding box overlap ratio


Getting Started with Point Clouds Using Deep Learning

Understand how to use point clouds for deep learning.

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®.

Choose Function to Visualize Detected Objects

Compare visualization functions.

Featured Examples