Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences

Akula, Aparna; Khanna, Nidhi; Ghosh, Ripul; Kumar, Satish; Das, Amitava; Sardana, Harish Kumar

doi:10.1016/j.infrared.2013.12.012

Cited by 68 publications

(19 citation statements)

References 14 publications

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“…Different methods are explored in this context. Most of them are classified as background subtraction methods [8][9][10][11][12], [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Where, the Background Modeling is the principal step in these different methods.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, this method statistically computes the temporal median value for each pixel in order to generate the adaptive background model. In the same context, the methods based on the statistical calculation have been developed [10], [11]. In [10], the background model is generated by computing the mean of the images in a time interval of video sequence.…”

Section: Related Workmentioning

confidence: 99%

“…In [10], the background model is generated by computing the mean of the images in a time interval of video sequence. In [11], the weighted mean is used to develop the background model. For each pixel, a threshold is also represented by the standard deviation in the same interval to decide if the pixel belongs to the moving object or to the background.…”

Section: Related Workmentioning

confidence: 99%

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A block-based background model for moving object detection

Elharrouss

Abbad

Moujahid

et al. 2017

ELCVIA

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Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, we compute the absolute difference between the background frame and each frame of sequence. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy.Key Words: Motion detection, Background subtraction, Background model, Background update, Video surveillance. IntroductionMotion detection is a paramount study field of computer vision. Its purpose is to extract the moving objects at time t in a video captured using a stationary camera. The motion detection is used for many applications. Among these applications there are video surveillance, human-machine interaction, the recognition of sign language specific to robotics applications and many others.The approaches mainly used for motion detection can be classified into three categories: the time difference methods, the analysis of optical flow, and the background subtraction methods. For the first one, the time difference being the calculation of the difference between two or more consecutive images in order to extract the moving area [1], [2], but the problem in this approach is that the detected objects are incomplete and poorly presented. The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A block-based background model for moving object detection

Elharrouss

Abbad

Moujahid

et al. 2017

ELCVIA

View full text Add to dashboard Cite

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“…1) A number of fixed Gauss distributions are established at each pixel point, which will consume a large number of system resources in processing. 2) When the illumination mutation occurs, it is prone to cause a large area of false positives (Akula et al, 2014a;Zhou et al, 2013a;Shimada et al, 2016a).For high-density and complex monitoring occasions, traditional video-based change detection is difficult to meet the application requirements. Although high speed ball camera is suitable for the monitoring of large areas and active targets, it is complex and expensive.…”

Section: Introductionmentioning

confidence: 99%

Infrared Rotation Plane Radar Change Detection

Wang

Zhang

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The existing change detection method mainly stays on the pixel-level, which is very susceptible to light, shadow, etc. And the complex calculation and analysis for each pixel reduce the detection efficiency. Moreover, there is no modeling determination method to initialize standard deviation of each element for existing mixed Gaussian background modeling methods. In this paper, an improved mixed Gaussian background modeling method is proposed, with the use of infrared rotation plane radar. The relationship between the corrected standard deviation of distance and the detection intensity is used to establish the curve of standard deviation of distance with detected intensity. For each data point, the standard deviation is initialized by the value estimated by the change curve, and the detected distance is used to establish the Gaussian mixture background model. The detection effect of the method is discussed and compared with the traditional Gauss background modeling in the experiment, the result shows that it has certain advantages in processing speed, adaptability to change background and accuracy of change detection.

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“…For example, Akula et al [12] used an initial set of frames without targets to construct a statistical background model and proposed an adaptive contour-based background subtraction technique for accurate moving target detection in infrared image sequences by producing binarized thin contour saliency map. Xu et al [13] intelligently combined the Lucas Kanade optical flow method and the frame differencing method to effectively detect infrared targets in simulations where the detector was either static or moving.…”

Section: Introductionmentioning

confidence: 99%

Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

et al. 2015

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Abstract:In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.

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Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences

Cited by 68 publications

References 14 publications

A block-based background model for moving object detection

A block-based background model for moving object detection

Infrared Rotation Plane Radar Change Detection

Two Algorithms for the Detection and Tracking of Moving Vehicle Targets in Aerial Infrared Image Sequences

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