Detecting small moving objects using temporal hypothesis testing

Tzannes, Alexis P.; Brooks, Dana H.

doi:10.1109/taes.2002.1008987

Cited by 63 publications

(15 citation statements)

References 17 publications

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“…The representative sequential detection methods are temporal hypothesis testing, 7 sequential hypothesis testing, 8,9 Hough transform (HT), 10,11 three-dimensional (3-D) matched filtering, 12 and energy accumulation. [13][14][15][16][17][18] Temporal hypothesis testing 7 is like a classifier. The algorithm has achieved good performance since those temporal profiles of pixels through which targets pass will be distinct from those through which clutters pass.…”

Section: Introductionmentioning

confidence: 99%

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

Shi

Yin³

et al. 2016

Opt. Eng

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, "Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities," Opt. Eng. Abstract. Dim targets are extremely difficult to detect using methods based on single-frame detection. Radiation accumulation is one of the effective methods to improve signal-to-noise ratio (SNR). A detection approach based on radiation accumulation is proposed. First, a location space and a motion space are established. Radiation accumulation operation, controlled by vectors from the motion space, is applied to the original image space. Then, a new image space is acquired where some images have an improved SNR. Second, quasitargets in the new image space are obtained by constant false-alarm ratio judging, and location vectors and motion vectors of quasitargets are also acquired simultaneously. Third, the location vectors and motion vectors are mapped into the two spaces, respectively. Volume density function is defined in the motion space. Location extremum of the location space and volume density extremum of motion space will confirm the true target. Finally, actual location of the true target in the original image space is obtained by space inversion. The approach is also applicable to detect multiple dim targets. Experimental results show the effectiveness of the proposed approach and demonstrate the approach is superior to compared approaches on detection probability and false alarm probability.

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Section: Introductionmentioning

confidence: 99%

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

Shi

Yin³

et al. 2016

Opt. Eng

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“…Silverman, Caefer, and Tzannes et al analyzed the temporal profiles of target and background [10,11]. Their works indicated that damped sinusoid filters [12,13], continuous wavelet transform [14], and hypothesis test performing on temporal profiles [15,16] are effective to detect dim point targets from evolving clutter. Subsequently, Lim et al develops an adaptive mean and variance filter for detecting dim point-like targets [17].…”

Section: Introductionmentioning

confidence: 99%

Novel Spatiotemporal Filter for Dim Point Targets Detection in Infrared Image Sequences

Wang

Zhang

et al. 2015

Journal of Electrical and Computer Engineering

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Dim point target detection is of great importance in both civil and military fields. In this paper a novel spatiotemporal filter is proposed to incorporate both the spatial and temporal features of moving dim point targets. Since targets are expected to be detected as far as possible, in this situation, they have no texture features in spatial dimensions, appearing like isolated points. Based on the attributes, potential targets are extracted by searching the local maximum point in a sliding window. And the potential targets are then correlated based on target moving patterns. After combining local maximum points and target moving patterns, structure background in infrared scene is removed. Next, the temporal profiles of infrared sense are reviewed and examined. By a new maxmedian filter performing on temporal profiles, the intensity of target pulse signal is extracted. Finally, each temporal profile is divided into several pieces to estimate the variance of the temporal profiles, which leads to a new detection metric. The proposed approach is tested via several infrared image sequences. The results show that our proposed method can significantly reduce the complex background in aerial infrared image sequence and have a good detection performance.

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“…Furthermore, aero-optic disturbances and air turbulence make the SNR of a single IR image very low (SNR < 3) in the real environments. Recently, many researchers have developed several spatial or temporal algorithms for the detection of moving targets in IR image sequences [4][5][6][7][8][9]. The spatial processing methods utilize the assumption that the target has a brighter intensity than the background, due to higher temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…To discriminate target and clutters in frequency domain, the cutoff frequency in Butterworth high-pass filter is decided by the WIE value of IR image. Tzannes et al proposed temporal filters using Mexican hat continuous wavelet transform (CWT) to temporal pixel profile in IR image sequences [7,8]. This method constructs a set of filters matched to point spread function (PSF) at different scales by selecting a mother wavelet function that has a shape like the PSF.…”

Section: Introductionmentioning

confidence: 99%

Small target detection using bilateral filter and temporal cross product in infrared images

Bae

2011

Infrared Physics & Technology

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Detecting small moving objects using temporal hypothesis testing

Cited by 63 publications

References 17 publications

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities

Novel Spatiotemporal Filter for Dim Point Targets Detection in Infrared Image Sequences

Small target detection using bilateral filter and temporal cross product in infrared images

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