Object Detection in Images and Videos Using OpenCV: A Comparative Study of Deep Learning and Traditional Computer Vision Techniques

Using OpenCV, this research compares the performance of deep learning with standard computer vision approaches for detecting object in photos and videos. Recognizing and localizing objects inside an image or video is a fundamental task in computer vision. Convolutional neural networks, for example, have recently demonstrated greater accuracy in object detection trials. Traditional computer vision approaches, such as the Viola-Jones algorithm, remain popular due to their ease of use and performance. In this work, we use OpenCV, a famous computer vision toolkit, to compare the efficacy of both of these methods. On diverse data sets, we evaluate the precision, speed, and complexity of each strategy and provide insights into the strengths and drawbacks of each method. This paper offers a complete overview of current object identification strategies and can assist researchers and practitioners in selecting the best effective approach for their individual application.