Optimization of Moving Objects Trajectory Using Particle Filter

The detection of foreign substances in injection so far is still achieved artificially, which result in low accuracy and low efficiency. This paper focuses on developing a novel vision-based approach for detection of foreign substances. Foreign substances are classified into two categories, subsiding-slowly object and subsiding-fast object. A relative movement caused by a motor helps to distinguished foreign substances from ampoule surface scratches. Moving objects in injection are divided from static ones by a background image derived from two frames. The Mean Shift Embedded Particle Filter (MSEPF) is proposed to detect moving-slowly object while Frame Distance is defined to detect moving-fast object. 200 ampoule samples filled with injection are tested. The integrated detection accuracy with this approach is 98.00%, with 97.56% accuracy for subsiding-slowly objects and 96.67% accuracy for subsiding-fast ones. The result shows that the system can detect foreign substances effectively.

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Flying object tracking and classification of military versus nonmilitary aircraft

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.1843 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1394-1403

Author(s):

Ehsan Akbari Sekehravani ◽

Eduard Babulak ◽

Mehdi Masoodi

Keyword(s):

Machine Vision ◽

Particle Filter ◽

Object Tracking ◽

Moving Objects ◽

Center Of Mass ◽

Speed Of Processing ◽

Detection And Tracking ◽

Particle Filter Algorithm ◽

Tracking Objects

Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the direction of motion of objects, and thermal imagery identifies the type of tracked flying objects. To demonstrate the efficiency and the strength of the algorithm and the above system, several scenarios in different videos have been investigated that include challenges such as the number of objects (aircraft), different paths, the diverse directions of motion, different speeds and various objects. One of the most important challenges is the speed of processing and the angle of imaging.

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A Hybrid Based Approach for Object Tracking in Video

IRA-International Journal of Technology & Engineering (ISSN 2455-4480) ◽

10.21013/jte.v4.n1.p2 ◽

2016 ◽

Vol 4 (1) ◽

Author(s):

Amith. R ◽

V.N. Manjunath Aradhya

Keyword(s):

Particle Filter ◽

Moving Objects ◽

Principal Component ◽

Discrete Wavelet ◽

Multi Scale ◽

Complex Background ◽

Video Frames ◽

Illumination Changes ◽

Wavelet Features ◽

Computer Vision Applications

<div><p><em>Tracking of moving objects in video sequences are essential for many computer vision applications & it is considered as a challenging research issue due to dynamic changes in objects, shape, complex background, illumination changes and occlusion. Many traditional tracking algorithms fails to track the moving objects in real-time, this paper proposes a robust method to overcome the issue, based on the combination of particle filter and Principal Component Analysis (PCA), which predicts the position of the object in the image sequences using stable wavelet features, which in turn are extracted from multi scale 2-D discrete wavelet transform. Later, PCA approach is used to construct the effective subspace. Similarity degree between the object model and the prediction obtained from particle filter is used to update the feature vector to handle occlusion and complex background in video frames. Experimental results obtained from the proposed method are encouraging.</em></p></div>

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