Abstract:

This paper explores various object detection (OD) techniques, focusing on the relationship between object identification, video analysis, and image understanding, which has gained significant research interest in recent years. Traditional object detection methods rely on high-quality features and shallow, teachable models. This survey highlights one such approach, the Optical Flow Method (OFM), which has proven to be robust and efficient for detecting moving objects, as demonstrated through an analysis presented in this paper. By applying optical flow to an image, flow vectors corresponding to the moving objects are generated. The next step involves marking the moving objects of interest for post-processing. Post-processing is a key contribution of this paper in addressing moving object detection challenges. The performance of these methods deteriorates when complex ensembles are formed, combining multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid advancement of deep learning, more powerful resources have emerged, capable of learning semantic and high-level features, addressing issues present in traditional approaches. These models differ in architecture, training strategies, and development work. This review also examines deep learning-based object detection techniques, beginning with an introduction to the history of deep learning and its representative tools, particularly Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (R-CNN).

Description:

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