Object Detection

Object detection in computer vision

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. Object detection is commonly used in applications such as image retrieval, security, surveillance, and automated vehicle parking systems.

You can detect objects in MATLAB^® with Computer Vision System Toolbox^™, and Image Processing Toolbox^™ using a variety of models, including:

• Extracted features and boosted learning algorithms
• Gradient-based and derivative-based approaches
• Viola-Jones algorithm
• HOG-based people detection
• Template matching approaches
• Image segmentation and blob analysis

Examples and How To

• Face Detection and Tracking (Example)
• Detecting a Cell Using Image Segmentation (Example)
• Barcode Recognition (Example)
• Traffic Warning Sign Recognition (Example)
• Computer Vision with MATLAB (Webinar)

Software Reference

• Object Detector (System Object)
• Object Detection, Motion Estimation, and Tracking (Product Information)
• Detect and Track Moving Objects Using Gaussian Mixture Models (Documentation)
• Feature Detection, Extraction, and Matching (Documentation)
• Count Objects in an Image (Documentation)

See also: Steve on Image Processing, image and video processing, MATLAB and OpenCV, object recognition, face recognition, image recognition