Refinement of Background-Subtraction Methods Based on Convolutional Neural Network Features for Dynamic Background

Abstract

Advancing the background-subtraction method in dynamic scenes is an ongoing timely goal for many researchers. Recently, background subtraction methods have been developed with deep convolutional features, which have improved their performance. However, most of these deep methods are supervised, only available for a certain scene, and have high computational cost. In contrast, the traditional background subtraction methods have low computational costs and can be applied to general scenes. Therefore, in this paper, we propose an unsupervised and concise method based on the features learned from a deep convolutional neural network to refine the traditional background subtraction methods. For the proposed method, the low-level features of an input image are extracted from the lower layer of a pretrained convolutional neural network, and the main features are retained to further establish the dynamic background model. The evaluation of the experiments on dynamic scenes demonstrates that the proposed method significantly improves the performance of traditional background subtraction methods.

Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow

“…Recently [3,4,16,17], convolutional neural networks (ConvNets) have presented excellent results in different vision challenges where it has shown an attractive characteristic to learn deep and hierarchical features, which make it more powerful than classical methods. In this work, two convolution layers, two max-pooling layers, and two fully connected feed-forward layers are adopted with the same network architecture in [16], which obtained better detection results by discriminating the foreground and background regions.…”

“…The most recent studies in intelligent transportation systems focus on vehicle detection [3,4,5,6,7,8,9,10,11,12]. Vehicle detection can be categorized into two groups [1]: detection methods based on vehicle appearance, and detection methods based on vehicle motion.…”