Background Subtraction for Moving Cameras

Droogenbroeck, Marc Van; Barnich, Olivier

doi:10.1201/b17223-10

Cited by 33 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For small sample size, this may lead to improper modelling of long‐term and short‐term periodic events. To address this problem, Barnich and Droogenbroeck [43, 44] presented a stochastic sampling technique where the background models are updated using a random replacement policy. We also follow this technique in our approach.…”

Section: Related Workmentioning

confidence: 99%

ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

Mandal¹,

Chaudhary²,

Vipparthi³

et al. 2019

IET Computer Vision

View full text Add to dashboard Cite

In this study, new feature descriptors are designed for medical image retrieval and change detection applications, respectively. Inspired by isomerism, the authors propose a novel feature descriptor named antithetic isomeric cluster pattern (ANTIC). The ANTIC is defined by the two properties: cluster patterns and antithetic isomerism (ANTI). The cluster pattern corresponds to successive pixel intensity differences at antithetical orientations. Furthermore, the ANTI is characterised by two aspects: first, the clusters are oppositely oriented (antithetical) to each other and second, both adhere to a defined isomeric property. The ANTIC identifies the lines and corner point information in the local neighbourhood across various directions. To attain enhanced robustness, they further proposed multiresolution ANTIC by integrating the multiresolution Gaussian filter. Moreover, to reduce the feature dimensionality, they extended their work to rotation invariant features. The proposed method outperforms other widely used feature descriptors in biomedical and retinopathy image retrieval applications. In addition, they extracted spatiotemporal features by designing intra-ANTIC and inter-ANTIC to detect motion changes in video sequences. They validated the effectiveness of these features by conducting experiments on CDNet 2014 dataset. The proposed descriptor achieves better performance in various challenging conditions for change detection as compared to other state-of-the-art techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

Mandal¹,

Chaudhary²,

Vipparthi³

et al. 2019

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…However, it requires much higher computational complexity compared to other methods. The recently developed ViBe algorithm may be applied to speed up the background subtraction process [12][13][14].…”

Section: Background Extractionmentioning

confidence: 99%

Vehicle Detection and Speed Estimation for Automated Traffic Surveillance Systems at Nighttime

2019

Teh. vjesn.

View full text Add to dashboard Cite

This article proposes a vehicle detection and speed measurement system to estimate a vehicle's velocity by identifying its headlight properties in a nighttime environment. We present a traffic surveillance system for vehicle detection and tracking in the nighttime along with a background extraction and automatic vanishing point detection process. We show that a single video camera can sufficiently and effectively operate to concurrently calculate and detect a vanishing point during the daytime. We have applied this to preprocessing in an automated traffic surveillance system in the nighttime. The experimental results show that vehicle tracking is possible, even in the nighttime. Preliminary experimental results confirm the possibility and effectiveness of the proposed algorithms for this nighttime vehicle surveillance system. They demonstrate that a single camera with an embedded image processing system can concurrently monitor, detect and track multiple vehicles in multiple lanes during the nighttime as successfully as the daytime.

show abstract

“…Optical flow method [6][7] requires a long calculation time and cannot meet the real-time requirements; The frame difference method [8], which is fast in calculation, has large errors. Vibe algorithm [9] is based on background subtraction, the main steps are as follows:…”

Section: Basicsmentioning

confidence: 99%

Detection and Analysis of Human Behavior in Video Monitoring

Li¹,

L²,

Peng³

et al. 2019

Proceedings of the 2019 International Conference on Wireless Communication, Network and Multimedia Engineering (WCNME 2019)

View full text Add to dashboard Cite

The research content of this paper is mainly aimed at the automatic recognition of human behavior in video surveillance. It usually contains two main steps: foreground detection and behavior recognition. In the foreground detection step, this paper studied related algorithms and theories, briefly analyzed the principles and the advantages and disadvantages of various algorithms, finally choose the Vibe algorithm to extract the motive targets, and made some improvements to the original version of Vibe, which can get a more satisfied result. In behavior recognition step, we roughly classified the existing algorithms into three categories: template matching method, state space method, and semantic description method. After analyzing these three types of algorithms, we decided to select the Support Vector Machine (SVM) for training. We used the Star model for the feature collection. After the simulation experiment, the recognition result is not bad.

show abstract

Background Subtraction for Moving Cameras

Cited by 33 publications

References 26 publications

ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

Vehicle Detection and Speed Estimation for Automated Traffic Surveillance Systems at Nighttime

Detection and Analysis of Human Behavior in Video Monitoring

Contact Info

Product

Resources

About