High-Resolution Bayes Detection of Dim Moving Point Target in IR Image Sequence Using Probabilistic Data Association Filter

Hamdulla, Askar; Lian, Xingke

doi:10.1109/csse.2008.329

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…The "track before detect" algorithm is a temporal based algorithm which uses multiple frames to incorporate temporal as well as spatial information. A lot of techniques and algorithms was developed within this concept of processing as 3-D matched filtering [8,9], Velocity filter banks [10,11], multistage IIR filter [12,13], Temporal Profile Based detection [14], Temporal filters [15,16], triple temporal filter (TTF) [17], bilateral TTF [18], dynamic programming [19,20], sequential detection [21], parallel spatial and temporal filtering [4,22] and Probabilistic data association (PDA) [23], etc.…”

Section: Introductionmentioning

confidence: 99%

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

Abdo¹,

Li²

2013

JSIP

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The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. In this paper we propose a new approach in processing of the Infrared image sequence for moving dim point targets detection built on the transformation of the IR-image sequence into 4-vectors for each frame in the sequence. The results of testing the proposed approach on a set of frames having a simple single pixel target performing a different motion patterns show the validity of the approach for detecting the motion, with simplicity in calculation and low time consumption.

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Section: Introductionmentioning

confidence: 99%

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

Abdo¹,

Li²

2013

JSIP

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Offline Performance Prediction of PDAF With Bayesian Detection for Tracking in Clutter

Zeng

Zheng

et al. 2013

IEEE Trans. Signal Process.

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A Flexible Algorithm for Detecting Challenging Moving Objects in Real-Time within IR Video Sequences

2017

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Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City.

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High-Resolution Bayes Detection of Dim Moving Point Target in IR Image Sequence Using Probabilistic Data Association Filter

Cited by 3 publications

References 4 publications

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

Offline Performance Prediction of PDAF With Bayesian Detection for Tracking in Clutter

A Flexible Algorithm for Detecting Challenging Moving Objects in Real-Time within IR Video Sequences

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