Visual Perception

Lane boundary, pedestrian, vehicle, and other object detections using machine learning and deep learning

You can detect objects using machine learning and deep learning techniques. You can also segment, detect, and model parabolic or cubic lane boundaries by using the random sample consensus (RANSAC) algorithm. After your detect objects, use Automated Driving Toolbox™ functions to evaluate and visualize the detections.

Functions

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Object Detection with Machine Learning

`peopleDetectorACF`	Detect people using aggregate channel features
`vehicleDetectorACF`	Load vehicle detector using aggregate channel features
`acfObjectDetector`	Detect objects using aggregate channel features
`configureDetectorMonoCamera`	Configure object detector for using calibrated monocular camera
`acfObjectDetectorMonoCamera`	Detect objects in monocular camera using aggregate channel features
`trainACFObjectDetector`	Train ACF object detector
`objectDetectorTrainingData`	Create training data for an object detector
`vision.PeopleDetector`	Detect upright people using HOG features
`vision.CascadeObjectDetector`	Detect objects using the Viola-Jones algorithm
`trainCascadeObjectDetector`	Train cascade object detector model

Object Detection with Deep Learning

`vehicleDetectorFasterRCNN`	Detect vehicles using Faster R-CNN
`fastRCNNObjectDetector`	Detect objects using Fast R-CNN deep learning detector
`fasterRCNNObjectDetector`	Detect objects using Faster R-CNN deep learning detector
`configureDetectorMonoCamera`	Configure object detector for using calibrated monocular camera
`fastRCNNObjectDetectorMonoCamera`	Detect objects in monocular camera using Fast R-CNN deep learning detector
`fasterRCNNObjectDetectorMonoCamera`	Detect objects in monocular camera using Faster R-CNN deep learning detector
`ssdObjectDetectorMonoCamera`	Detect objects in monocular camera using SSD deep learning detector
`yolov2ObjectDetectorMonoCamera`	Detect objects in monocular camera using YOLO v2 deep learning detector
`trainFasterRCNNObjectDetector`	Train a Faster R-CNN deep learning object detector
`trainFastRCNNObjectDetector`	Train a Fast R-CNN deep learning object detector
`vehicleDetectorYOLOv2`	Detect vehicles using YOLO v2 Network
`trainYOLOv2ObjectDetector`	Train YOLO v2 object detector
`objectDetectorTrainingData`	Create training data for an object detector

Lane Boundary Detection and Modeling

`segmentLaneMarkerRidge`	Detect lanes in a grayscale intensity image
`findParabolicLaneBoundaries`	Find boundaries using parabolic model
`parabolicLaneBoundary`	Parabolic lane boundary model
`findCubicLaneBoundaries`	Find boundaries using cubic model
`cubicLaneBoundary`	Cubic lane boundary model
`computeBoundaryModel`	Obtain y-coordinates of lane boundaries given x-coordinates
`insertLaneBoundary`	Insert lane boundary into image
`fitPolynomialRANSAC`	Fit polynomial to points using RANSAC
`ransac`	Fit model to noisy data

Evaluate Detections

`evaluateDetectionPrecision`	Evaluate precision metric for object detection
`evaluateDetectionMissRate`	Evaluate miss rate metric for object detection
`evaluateLaneBoundaries`	Evaluate lane boundary models against ground truth

Visualize Detections

`insertText`	Insert text in image or video
`insertShape`	Insert shapes in image or video
`insertMarker`	Insert markers in image or video
`insertLaneBoundary`	Insert lane boundary into image
`insertObjectAnnotation`	Annotate truecolor or grayscale image or video stream
`vision.DeployableVideoPlayer`	Display video
`vision.VideoPlayer`	Play video or display image

Featured Examples

Visual Perception Using Monocular Camera

Construct a monocular camera sensor simulation capable of lane boundary and vehicle detections.

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Generate Code for Lane Marker Detector

Test a monocular-camera-based lane marker detector and generate C++ code for real-time applications on a prebuilt 3D scene from the Unreal Engine® driving simulation environment. This example validates the lane marker detector algorithm using metrics and verifies the generated C++ code by using software-in-the-loop simulation.

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