Background subtraction has been widely used in the detection of a moving object from a still scene. Due to the uncertainty in the classification of the pixels in the foreground and background, we propose a novel fuzzy approach for background subtraction using fuzzy histograms based on fuzzy c-means clustering and the fuzzy nearness degree, called FCFN. In this method, the temporal characteristics of the pixels are described by a fuzzy histogram using the fuzzy c-means algorithm. The segmentation threshold is adaptively calculated according to the distribution of the fuzzy nearness degree of individual pixel. Fuzzy adaptive background maintenance is adopted in the background update framework. The performance of the FCFN is evaluated against several state-of-the-art methods in the complex dynamic scenes. The experimental results demonstrate that the proposed method doubles the improvements in performance than the classic fuzzy background modeling methods and outperforms most state-of-the-art methods.

Background subtraction plays a fundamental role for anomaly detection in video surveillance, which is able to tell where moving objects are in the video scene. Regrettably, the regular rotating pumping unit is treated as an abnormal object by the background-subtraction method in pumping-unit surveillance. As an excellent classifier, a deep convolutional neural network is able to tell what those objects are. Therefore, we combined background subtraction and a convolutional neural network to perform anomaly detection for pumping-unit surveillance. In the proposed method, background subtraction was applied to first extract moving objects. Then, a clustering method was adopted for extracting different object types that had more movement-foreground objects but fewer typical targets. Finally, nonpumping unit objects were identified as abnormal objects by the trained classification network. The experimental results demonstrate that the proposed method can detect abnormal objects in a pumping-unit scene with high accuracy.

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.