Abstract
This paper India delves into the dynamic field of object detection in computer vision and image processing, specifically focusing on recognizing individuals in photographs. Employing the OpenCV library, the study employs two distinct approaches: the first utilizes Haar Cascade Classifiers, a straightforward yet effective method for object detection, particularly well-suited for person recognition. The second approach harnesses the capabilities of the Single Shot Multi-Box Detector (SSD), a state-of-the-art technique known for its real-time object detection prowess, combining high accuracy and speed. By integrating these two approaches, the paper proposes a comprehensive strategy for person detection in photos. Haar Cascade Classifiers provide a simple yet efficient foundation, while the sophistication of SSD, driven by deep learning principles, enhances accuracy and efficiency. This hybrid model offers a holistic solution applicable to diverse contexts, such as surveillance, image analysis, and broader applications within the realm of computer vision.
Keywords: Detect bounding boxes, Detect multiScale, HAAR cascade classifier, Load.