Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field

Crivelli, Tomas; Bouthémy, Patrick; Cernuschi-Frias, B.; Yao, Jianfeng

doi:10.1007/s11263-011-0429-z

Cited by 23 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1) Temporal saliency: Detection of salient regions is highly dependent on the recognition of moving objects since motions attract more attention [18]. For moving object detection from a video, moving object detection methods are mainly based on temporal information, such as background subtraction [19,20], frame difference [21,22], and optical flow [23].…”

Section: A Visual Saliency-based Approachmentioning

confidence: 99%

A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

Pei¹,

Zhang²

2023

IJACSA

View full text Add to dashboard Cite

Visual tracking in uncrewed aerial vehicles is challenging because of the target appearance. Various research has been fulfilled to overcome appearance variations and unpredictable moving target issues. Visual saliency-based approaches have been widely studied in biologically inspired algorithms to detect moving targets based on attentional regions (ARs) extraction. This paper proposes a novel visual tracking method to deal with these issues. It consists of two main phases: spatiotemporal saliency-based appearance modeling (SSAM) and sample feature-based target detection (SFTD). The proposed method is based on a tracking-by-detection approach to provide a robust visual tracking system under appearance variation and unpredictable moving target conditions. Correspondingly, a semi-automatic trigger-based algorithm is proposed to handle the phases' operation, and a discriminative-based method is utilized for appearance modeling. In the SSAM phase, temporal saliency extracts the ARs and coarse segmentation. Spatial saliency is utilized for the object's appearance modeling and spatial saliency detection. Because the spatial saliency detection process is timeconsuming for multiple target tracking conditions, an automatic algorithm is proposed to detect the region saliences in a multithreading implementation that leads to low processing time. Consequently, the temporal and spatial saliencies are integrated to generate the final saliency and sample features. The generated sample features are transferred to the sample feature-based target detection (SFTD) phase to detect the target in different images based on samples. Experimental results demonstrate that the proposed method is effective and presents promising results compared to other existing methods.

show abstract

Section: A Visual Saliency-based Approachmentioning

confidence: 99%

A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

Pei¹,

Zhang²

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In the literature, we can conclude that the noise is always treated in one of two ways, either explicitly or implicitly. The explicit way [11,24,31,35,38,39,40,41,42,43] means that the noise is modeled by using some measures, for instance a Gaussian distribution assumption. Tavakkoli et al.…”

Section: Related Workmentioning

confidence: 99%

“…Crivelli et al. [41] proposed a so-called mixed state statistical framework. In this framework, they thought that background intensity values were perturbed by the Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

A Novel and Practical Scheme for Resolving the Quality of Samples in Background Modeling

Zhang

Yuan

Tong

et al. 2019

Sensors

View full text Add to dashboard Cite

In view of its important application value, background modeling is studied so widely that many techniques have emerged, which mainly concentrate on the selections of the basic model, the granularity of processing, the components in a framework, etc. However, the quality of samples (QoS) for training has long been ignored. There are two aspects regarding this issue, which are how many samples are suitable and which samples are reliable. To tackle the “how many” problem, in this paper, we propose a convergent method, coined Bi-Variance (BV), to decide an appropriate endpoint in the training sequence. In this way, samples in the range from the first frame to the endpoint can be used for model establishment, rather than using all the samples. With respect to the “which” problem, we construct a pixel histogram for each pixel and subtract one from each bin (called number of intensity values (NoIV-1)), which can efficiently get rid of outliers. Furthermore, our work is plug-and-play in nature, so that it could be applied to diverse sample-based background subtraction methods. In experiments, we integrate our scheme into several state-of-the-art methods, and the results show that the performance of these methods in three indicators, recall, precision, and F-measure, improved from 4.95% to 16.47%, from 5.39% to 26.54%, and from 12.46% to 20.46%, respectively.

show abstract

“…In order to minimize the computational load, we extract first the binary mask of moving objects in every frame by means of a motion detection algorithm. To this end, we use our motion detection method by background subtraction described in Crivelli et al (2011) which also built upon (Veit et al, 2011). We denote by Υ(t) the set of moving pixels extracted at time instant t, with Υ(t) ⊂ Ω.…”

Section: Affine Motion Modelsmentioning

confidence: 99%

Detection and Localization of Anomalous Motion in Video Sequences from Local Histograms of Labeled Affine Flows

2017

Self Cite

View full text Add to dashboard Cite

Simultaneous Motion Detection and Background Reconstruction with a Conditional Mixed-State Markov Random Field

Cited by 23 publications

References 49 publications

A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

A Novel and Practical Scheme for Resolving the Quality of Samples in Background Modeling

Detection and Localization of Anomalous Motion in Video Sequences from Local Histograms of Labeled Affine Flows

Contact Info

Product

Resources

About