Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection

Abstract

Cast shadow detection and removal is one of the key problems in vision-based systems for accurate and robust segmentation of moving objects. This paper proposes a cotraining-based adaptive method for detecting moving shadows in video sequences. Shadow detection based on static methods cannot adapt to changing environment such as gradual illumination changes. In order to solve this problem, we have proposed an online sequential extreme learning machine (OS-ELM)-based semi-supervised technique for moving cast shadow detection. Online learning of OS-ELM is much faster and provides better generalization performance compared to other popular online learning algorithms. First, we extracted color, texture, gradient, and image patch similarity features using a background model and input video frame, which are useful for discriminating moving shadows and objects. Co-training scheme is used for online updating of the OS-ELM classifier in order to adapt to the dynamic environment. Experimental results on different benchmark video sequences shows that the proposed method performs better shadow detection and discrimination compared with other methods.

An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

“…After feature extraction, the objective of the next step is to assign each pixel to one of two categories: shadow and object. The ELM [43][44][45][46][47][48] is adopted for classification, which has been proven to be effective and efficient in addressing a variety of classification problems. Next, the classification procedure using the ELM algorithm is described in detail.…”

“…As a special type of single-hidden layer feed-forward neural network, an extreme learning machine (ELM) has been extensively applied in several fields of machine learning [43][44][45][46][47][48], in which the parameters are randomly generated and the values of parameters do not need to be tuned. Ghimire and Lee [48] proposed an online sequential extreme learning machine-based semi-supervised technique for moving cast shadow detection, which provided better generalization performance. Motivated by its fast learning, good generalization and universal approximation capability, we propose an effective moving shadow detection based on the extreme learning machine.…”