Object detection and tracking systems make excellent computer vision projects. You can build a system that identifies and tracks objects in real-time using frameworks like OpenCV and YOLO. Projects might include tracking vehicles in traffic footage, counting people in spaces, or monitoring manufacturing lines for quality control. These projects teach core concepts like image processing, neural network architectures, and real-time video analysis.
Facial recognition and emotion detection systems combine multiple computer vision techniques. Using libraries like dlib and face_recognition, you can create applications that detect faces, recognize individuals, or analyze expressions. These projects involve working with facial landmarks, feature extraction, and classification algorithms. They're particularly useful for learning about data preprocessing and model training.
Document analysis and OCR systems help automate paper-to-digital conversion. Using tools like Tesseract and OpenCV, you can build systems that extract text from images, process handwritten documents, or organize scanned papers. These projects teach important skills like image preprocessing, text detection, and post-processing techniques. They also offer practical experience with handling real-world data variations and noise.