Moving Object Detection Based on Background Compensation and Deep Learning

Juncai Zhu,Shuli Chen,Zhizhong Wang,Songwei Wang

doi:10.3390/sym12121965

Abstract

Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

Highlights

Moving object detection and recognition is an important research area in the field of computer vision, where it plays important roles in intelligent video surveillance [1,2], robot vision navigation [3,4], virtual reality [5], and medical diagnosis [6]
In order to verify the performance of the object detection model for a motion scene, we have collected some images from other datasets and the Internet, most of which were taken from drones and distant places
Our method uses the background compensation method to detect moving regions and this method is combined with the lightweight YOLOv3-Small Objects Detection (SOD) network with a high capacity for detecting small targets, thereby allowing the positions of moving objects to be accurately detected

Summary

Introduction

Moving object detection and recognition is an important research area in the field of computer vision, where it plays important roles in intelligent video surveillance [1,2], robot vision navigation [3,4], virtual reality [5], and medical diagnosis (cell state tracking) [6]. In contrast to the moving object detection algorithms used for fixed cameras, such as optical flow [9], inter-frame difference [10], and background modeling [11], the image background often appears to undergo rotation, translation, and scaling when employing moving cameras. It is difficult to model the background or detect moving targets based on the optical flow. To solve this problem, we propose a moving object detection method by combining background compensation [12,13,14] and deep learning [15,16,17].

Methods

Results

Conclusion