Detection of Moving Objects by Background Subtraction for Foreground Detection

EasyChair Preprint no. 7559

Abstract

Background subtraction for foreground detection has been commonly applied for varying usages to identify objects in motion within a scene, such as that in video surveillance. In fact, significant publications were noted in the last decade within this area of background modelling. Despite the several surveys noted in the literature, none has offered a comprehensive review in this field. Therefore, this paper elaborates both conventional and recent approaches in light of background modelling. Initially, the approaches listed in the literature were classified in terms of mathematical models. Next, these models were analyzed based on challenging scenarios that they managed, the challenges and issues are then summarized. After that, an enhanced method is proposed, resulting from hybridizing the weight optimizations from CNN with a set of customized features derived by the Viola‐ Jones detector to enhance the overall network's performance. The initial findings show that the proposed method superior the state-of-the-art models in terms of accuracy, recall, precision, and F-measure.

Keyphrases: background subtraction, foreground detection, moving objects, Object detection.

@Booklet{EasyChair:7559,
  author = {Mukarram Safaldin and Nizar Zaghden},
  title = {Detection of Moving Objects by Background Subtraction for Foreground Detection},
  howpublished = {EasyChair Preprint no. 7559},

  year = {EasyChair, 2022}}