A COLOR FEATURES-BASED METHOD FOR OBJECT TRACKING EMPLOYING A PARTICLE FILTER ALGORITHM

2009 ◽  
Author(s):  
Budi Sugandi ◽  
Hyoungseop Kim ◽  
Joo Kooi Tan ◽  
Seiji Ishikawa ◽  
Abdul Halim Hakim ◽  
...  
Author(s):  
Norikazu Ikoma ◽  
◽  
Akihiro Asahara ◽  

Real time visual tracking by particle filter has been implemented on Cell Broadband Engine in parallel. Major problem for the implementation is small size of Local Store (LS) in SPEs (Synergistic PEs), which are computational cores, to deal with image of large size. As a first step for the implementation, we focus on color single object tracking, which is one of the most simple case of visual tracking. By elaborating to compress the color extracted image into bit-wise representation of binary image, all information of the color extracted image can be stored in LS for 640×480 size of original image. By applying our previous implementation of general particle filter algorithm on Cell/B.E. to this specific case, we have achieved real time performance of visual tracking on PlayStation®3 about 7 fps with a camera of maximum 15 fps.


2014 ◽  
Vol 556-562 ◽  
pp. 2702-2706
Author(s):  
Ying Xia ◽  
Xin Hao Xu

Accuracy and stability is crucial for dynamic object tracking. Considering the scale invariance, rotational invariance and strong anti-jamming capability of KAZE features, a method of dynamic object tracking based on KAZE features and particle filter is proposed. This method obtains the global color features of the dynamic object appearance and extracts its local KAZE features to construct the object model first, and then performs dynamic tracking by particle filter. Experimental results demonstrate the accuracy and stability of the proposed method.


2020 ◽  
Vol 9 (4) ◽  
pp. 1394-1403
Author(s):  
Ehsan Akbari Sekehravani ◽  
Eduard Babulak ◽  
Mehdi Masoodi

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.


2014 ◽  
Vol 926-930 ◽  
pp. 3141-3144 ◽  
Author(s):  
Jiai He ◽  
Yong Na Li

With the robustness of a single color which is not high in standard particle filter tracking, a fusion of color and gradient particle filter algorithm is proposed. By the advantages of color described the target ’global and gradients described the shape of structure, they are weighted fusion to form a new integrated histogram and applied to the particle filter framework. The experimental results show that compared with the traditional particle filter algorithm, the text of the algorithm can achieve relatively reliable target tracking under complicated background and illumination changes, with better robustness and reliability.


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