Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

by   Jhony H. Giraldo, et al.

Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to address MOD with competitive performance. However, in order to handle the overfitting problem, deep learning methods require a large amount of labeled data which is a laborious task as exhaustive annotations are always not available. Moreover, some MOD deep learning methods show performance degradation in the presence of unseen video sequences because the testing and training splits of the same sequences are involved during the network learning process. In this work, we pose the problem of MOD as a node classification problem using Graph Convolutional Neural Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance segmentation, background initialization, feature extraction, and graph construction. GraphMOD-Net is tested on unseen videos and outperforms state-of-the-art methods in unsupervised, semi-supervised, and supervised learning in several challenges of the Change Detection 2014 (CDNet2014) and UCSD background subtraction datasets.


page 2

page 7

page 8


A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos

Background subtraction is a basic task in computer vision and video proc...

Unsupervised RGBD Video Object Segmentation Using GANs

Video object segmentation is a fundamental step in many advanced vision ...

GraphBGS: Background Subtraction via Recovery of Graph Signals

Graph-based algorithms have been successful approaching the problems of ...

Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Object detection is a fundamental step for automated video analysis in m...

BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-AgnosticSupervised Background Subtraction

Background subtraction (BGS) is a fundamental video processing task whic...

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Computer vision applications based on videos often require the detection...

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

In recent years, deep learning has been successfully applied in various ...

Please sign up or login with your details

Forgot password? Click here to reset