Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos

Abstract

Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.