Moving Object Tracking Using Symmetric Mask-Based Scheme

Abstract

This work presents a new approach, symmetric mask-based scheme (SMBS), for moving object detection and tracking based on the symmetric mask-based discrete wavelet transform (SMDWT). This work presents a fast algorithm, called 2-D SMDWT, to improve the critical issue of the 2-D lifting-based Discrete Wavelet Transform (LDWT), and then obtains the benefit of low latency, reduced complexity, and low transpose memory for object detection. The successful moving object detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. The SMBS approach can effectively reduce noises with low computing cost in both indoor and outdoor environments. The experimental results indicate that the proposed method can provide precise moving object detection and tracking.