Low resolution method using adaptive LMS scheme for moving objects detection and tracking

Abstract

This paper presents a new model for adaptive filter with the least-mean-square (LMS) scheme to train the mask operation on low resolution images. The adaptive filter theory with adaptive least-mean-square scheme (ALMSS) uses the training mask for moving object detection and tracking. However, the successful moving objects detection in a real surrounding environment is a difficult task due to noise issues such as fake motion or Gaussian noise. Many approaches have been developed in constrained environments to detect and track moving objects. On the other hand, the ALMSS approach can effectively reduce the noise with low computing cost in both fake motion and Gaussian noise environments. The experiments on real scenes indicate that the proposed ALMSS method is effective for moving object detection and tracking in real-time.

Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

“…LMS algorithm is relatively simple, has much lower computational complexity than other adaptive algorithms; it does not require correlation function calculation nor does it require matrix inversions [15,16]. LMS has been widely used in several real time image applications such as motion estimation and target tracking , where it showed robustness on fast moving targets and non-linear moving targets even in noisy environments as reported by the authors in [42][43][44][45]. The LMS implementation process has been illustrated in Fig.…”