A sequential detection approach to target tracking

Blostein, Steven D.; Richardson, H.S.

doi:10.1109/7.250420

Cited by 52 publications

(20 citation statements)

References 35 publications

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“…Blostein SD [5] and so on are based on the sequential hypothesis testing method, which belongs to the exhaustive searching method. All the possible trajectories in the image sequence are calculated.…”

Section: Advances In Engineering Volume 126mentioning

confidence: 99%

Study of Low-altitude Slow and Small Target Detection on Radar

Xu¹,

Zhang²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract. Holding low-altitude, slow and small (LSS) target has become a difficulty and study hotspot in air defense warning. This paper firstly introduces the characteristics of the LSS-target and the practical significance and military requirements for detecting such target, and briefly describes the difficulty of detecting radar targets. Then, finally, the prospects for the study of the LSS-target detection are presented.

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Section: Advances In Engineering Volume 126mentioning

confidence: 99%

Study of Low-altitude Slow and Small Target Detection on Radar

Xu¹,

Zhang²

2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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“…When operating in the visible spectrum, peaks are typically identified using statistical change detectors [11,12] to suppress clutter (on background features of no interest). Trackers based on algorithms such as Probabilistic Data Association (PDA) [5][6][7][8]13], Joint Probabilistic Data Association (JPDA) [9,14], or Multiple Hypothesis Tracking (MHT) [10,15,16], are then applied.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Tracking Moving Objects in Sequences of Color Images

Kennedy¹

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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A statistical change detector, implemented as a zero-latency finitememory filter, is used to identify anomalies in temporal pixel statistics. An F-distributed test statistic is computed for each pixel and used in a hypothesis test. The tracker, with automatic track initiation and termination, uses a low-complexity pairwise Joint Probabilistic Data Association (JPDA) algorithm, which has been restricted to consider clusters (sub-problems) containing no more than two tracks. The track state and clutter model are augmented to include color. The detector and tracker are used to process sample video data.

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“…Recently, there has been a growing interest in detection of small low-contrast objects from optical or infrared (IR) image sequences [1][2][3][4][5][6][7][8][9][10][11][12]. The problem of detecting small low-contrast objects arises, for example, when we detect small moving targets from an on-board JR sensor [6,12], and when we detect meteors, asteroids and satellites in the night sky from an array of charge coupled device (CCD) sensors on the focal plane of a telescope [1,8,11].…”

Section: Introductionmentioning

confidence: 99%

<title>Small target detection from image sequences using recursive max filter</title>

1995

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This paper presents a new method for detection of small moving targets from noisy image sequences using a recursive filter, which contains a local maximum filter in the feedback loop. Since all the procedures contained in this method can be implemented by fully parallel algorithm, this method can be realized by a neural network and real-time processing is possible for image sequences at high frame rates. First we investigate the performance of the method on the assumption that the targets move randomly in an image plane and the background noise is white both in time and space. The results show that the proposed method has the ability to suppress the background noise and enhances small moving targets, and that the targets are detected only by thresholding after the processing of the recursive filter. There can be other backgrounds such as clutters and stationary objects. It is also shown that the above method is applicable after preprocessing of conventional time difference method, and is able to suppress these backgrounds. Next we analyze the performance of the method using the property of order statistics. The performance measure is the output signal-to-noise ratio (SNR) and is represented analytically as a function of input SNR and the parameters included in the method. Finally, the recursive filter is optimized using the result of the analysis.

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A sequential detection approach to target tracking

Cited by 52 publications

References 35 publications

Study of Low-altitude Slow and Small Target Detection on Radar

Study of Low-altitude Slow and Small Target Detection on Radar

Detecting and Tracking Moving Objects in Sequences of Color Images

<title>Small target detection from image sequences using recursive max filter</title>

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