Detect people using aggregate channel features
detector = peopleDetectorACF
detector = peopleDetectorACF(name)
Load the upright people detector.
detector = peopleDetectorACF;
Read an image. Detect people in the image.
I = imread('visionteam1.jpg'); [bboxes,scores] = detect(detector,I);
Annotate detected people with bounding boxes and their detection scores.
I = insertObjectAnnotation(I,'rectangle',bboxes,scores); figure imshow(I) title('Detected People and Detection Scores')
name— ACF classification model
ACF classification model, specified as
'inria-100x41' model was trained using the
INRIA Person data set. The
was trained using the Caltech Pedestrian data set.
 Dollar, P., R. Appel, S. Belongie, and P. Perona. "Fast Feature Pyramids for Object Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 36, Issue 8, 2014, pp. 1532–1545.
 Dollar P., C. Wojek, B. Shiele, and P. Perona. "Pedestrian Detection: An Evaluation of the State of the Art." IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 34, Issue 4, 2012, pp. 743–761.
 Dollar, P., C., Wojek, B. Shiele, and P. Perona. "Pedestrian Detection: A Benchmark." IEEE Conference on Computer Vision and Pattern Recognition. 2009.