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`	(Removed) 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
`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 (Since R2020a)
`yolov2ObjectDetectorMonoCamera`	Detect objects in monocular camera using YOLO v2 deep learning detector
`yolov3ObjectDetectorMonoCamera`	Detect objects in monocular camera using YOLO v3 deep learning detector (Since R2023a)
`yolov4ObjectDetectorMonoCamera`	Detect objects in monocular camera using YOLO v4 deep learning detector (Since R2022a)
`vehicleDetectorYOLOv2`	Detect vehicles using YOLO v2 Network (Since R2020a)
`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

`evaluateObjectDetection`	Evaluate object detection data set against ground truth (Since R2023b)
`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
`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

Generate C++ code for lane marker detector and validate the functional equivalence using software-in-the-loop (SIL) simulation.

Open Script

Automate Testing for Lane Marker Detector

Automate the testing of a lane marker detector algorithm and generated code.

Open Script

Train a Deep Learning Vehicle Detector

Train a vision-based vehicle detector using deep learning.

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Generate Code for Vision Vehicle Detector

Generate deployable code for a monocular-camera-based vehicle detector and validate the functional equivalence with simulation.

Open Example

Automate Testing for Vision Vehicle Detector

Automate the testing of a vehicle detector and generated code.

Open Script

Track Multiple Vehicles Using a Camera

Detect and track multiple vehicles with a monocular camera mounted in a vehicle.

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Perception-Based Parking Spot Detection Using Unreal Engine Simulation

Build a bird's-eye-view map of a parking lot using semantically segmented images from the ego vehicle camera, and detect empty parking spots from the map.

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Perception Based Live Parking Spot Detection Using Unreal Engine Simulation

Develop a live parking spot detection system using deep learning and SLAM.

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