Shadow Elimination Algorithm Using Color and Texture Features

Abstract

Shadow detection and removal in real scene images are a significant problem for target detection. This work proposes an improved shadow detection and removal algorithm for urban video surveillance. First, the foreground is detected by background subtraction and the shadow is detected by HSV color space. Using local variance and OTSU method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, we embed our algorithm into C/S framework based on the HTML5 web socket protocol. Both the experimental and actual operation results show that the proposed algorithm is efficient and robust in target detection and shadow detection and removal under different scenes.

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

“…The simulation results indicate that the proposed algorithms are effective. What's more, the scientific idea of this work to discuss the problems of parameter estimation for multivariable systems can be expanded to different systems with coloured noises [74,75] and can handle the identification problems of some applied science and engineering [76][77][78][79] such as communication networked systems and power and information systems [80][81][82][83].…”

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

“…In the future work, we will further investigate whether these algorithms can be applied to systems with missing data. The iterative algorithm in this paper is proposed for bilinear stochastic systems but the idea can be extended to other linear and nonlinear stochastic systems with colored noises [66][67][68][69][70][71][72][73][74][75] and can be applied to other literatures [76][77][78][79][80][81][82][83] such as signal modeling, pattern cognition, information processing, and engineering application systems.…”

Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise

“…Moreover, the recursion computation of 2S-LS-MIESG algorithm involves more innovations and data. The proposed 2S-LS-MIESG algorithm in this article can joint some mathematical strategies 59,60 to explore new identification methods of linear and nonlinear stochastic systems [61][62][63][64] and can applied to other fields. [65][66][67][68] The pseudo-code of implementing the 2S-LS-MIESG algorithm is shown in Algorithm 2.…”