Object detection is the process of finding instances of real-world objects such as faces, bicycles, and buildings in images or videos. Object detection algorithms typically use extracted features and learning algorithms to recognize instances of an object category. It is commonly used in applications such as image retrieval, security, surveillance, and automated vehicle parking systems.
You can detect objects using a variety of models, including:
Face detection (left) and stop sign detection (right) using the Viola-Jones Object Detector.
Human detection using pretrained SVM with HOG features.
Moving cars are detected using blob analysis.
Image segmented using background subtraction. The moving pixels (foreground) detected from the video frame above are shown in white.
See also: Statistics and Machine Learning Toolbox, Computer Vision System Toolbox, Steve on Image Processing, image processing and computer vision, MATLAB and OpenCV, object recognition, face recognition, image recognition, Feature Extraction, Stereo Vision, Optical Flow, ransac, pattern recognition, object detection videos, point cloud, deep learning