A Cost-Effective Method for Detecting and Tracking Moving Objects using Overlapping Methods

Overlay approaches for moving object detection and tracking have recently received attention as a crucial field for computer science and computer vision research. Using pixel overlap and visual attributes, these techniques enable the recognition and tracking of objects in movies or video data. Two color and edge features for the suggested method are presented in this article. The suggested approach uses the SED algorithm, and since the edges have a lower volume than the entire image, the processing process will be faster with the reduction of information. The characteristic of color is the HSV (hue, saturation and value) histogram because it is close to human vision. However, because the margins tidy up the shapes in the human eye, they contain important information. These concerns lead to the conclusion that the histogram of gradient angles based on regional binary patterns is the edge feature of the suggested system. There are two justifications for employing local binary patterns. First, the principal edges are emphasized by using local binary patterns. Another point is that the image produced by this method displays the image's texture; in other words, the shape's feature is taken from the context of the texture, which is regarded as a type of combination of features. Several criteria were evaluated in order to assess the suggested approach for tracking images in comparison to related systems; the most significant of these are the precision, recall, and similarity criteria. In comparison to other works, the findings for precision have generally increased accuracy by 25%, recall by 17%, and similarity by 12%.

@article{ZHANG2023,
  title     = {A Cost-Effective Method for Detecting and Tracking Moving Objects using Overlapping Methods},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yuanyuan ZHANG},
  doi       = {10.14569/IJACSA.2023.0141076},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141076}
}