An Object Detection Method Using Wavelet Optical Flow and Hybrid Linear-Nonlinear Classifier

Pengcheng Han,Suguo Zhu,Junping Du,Jun Zhou

doi:10.1155/2013/965419

Abstract

We propose a new computational intelligence method using wavelet optical flow and hybrid linear-nonlinear classifier for object detection. With the existing optical flow methods, it is difficult to accurately estimate moving objects with diverse speeds. We propose a wavelet-based optical flow method, which uses wavelet decomposition in optical flow motion estimation. The algorithm can accurately detect moving objects with variable speeds in a scene. In addition, we use the hybrid linear-nonlinear classifier (HLNLC) to classify moving objects and static background. HLNLC transforms a nonoptimal scalar variable into its likelihood ratio and uses a scalar quantity as the decision variable. This approach is appropriate for the classification of optical flow feature vectors with unequal variance matrices. The experimental results confirm that our proposed object detection method has an improved accuracy and computation efficiency over other state-of-the-art methods.

Highlights

In modern engineering, the requests for research and design are increasingly achieved with the help of intelligent models
The experimental results confirm that our proposed object detection method has an improved accuracy and computation efficiency over other state-of-the-art methods
Video (b) is a standard video compression sequence known as the coastguard sequence, where a video camera is fixed on a moving boat so it appears that the background is moving; it has 876 frames of 768 × 576 resolution

Summary

Introduction

The requests for research and design are increasingly achieved with the help of intelligent models. We propose a new computational intelligence method using wavelet optical flow and hybrid linear-nonlinear classifier (HLNLC) for object detection. When the image background is cluttered or the detected object is moving at high speed, the accuracy of gradient-based methods will be significantly decreased [10]. Another important task in object detection is classification. We propose a new object detection method using wavelet-based optical flow and hybrid linear-nonlinear classifier. Our proposed method could achieve an accelerated optical flow computation and accurately estimate the motions of different speed moving objects in the same scenes.

Wavelet Based Optical Flow

Classification

Rectangle Window Scan

Experimental Results

Conclusions