A Genetic Algorithm for Target Tracking in FLIR Video Sequences Using Intensity Variation Function

2009 ◽  
Vol 58 (10) ◽  
pp. 3457-3467 ◽  
Author(s):  
G. Paravati ◽  
A. Sanna ◽  
B. Pralio ◽  
F. Lamberti
Keyword(s):  
Genetic Algorithm ◽  
Target Tracking ◽  
Intensity Variation ◽  
Video Sequences ◽  
Variation Function

TARGET TRACKING IN VIDEO SEQUENCES USING WAVELET TRANSFORM

2008 ◽  
Vol 06 (05) ◽  
pp. 675-690 ◽  
Author(s):  
S. ARIVAZHAGAN ◽  
W. SYLVIA LILLY JEBARANI ◽  
G. KUMARAN
Keyword(s):  
Wavelet Transform ◽  
Target Tracking ◽  
Tracking System ◽  
Region Growing ◽  
Video Sequences ◽  
Discrete Wavelet ◽  
Perspective View ◽  
Cluttered Environment ◽  
Detection And Tracking ◽  
Video Frames

Automatic target tracking is a challenging task in video surveillance applications. Here, an offline target-tracking system in video sequences using Discrete Wavelet Transform is presented. The proposed algorithm uses co-occurrence features, derived from sub-bands of discrete wavelet transformed sub-blocks, obtained from individual video frames, to identify a seed in the frame. Then, the region-growing algorithm is applied to detect and track the target. The results of the proposed target detection and tracking system in video sequences are found to be satisfactory. The effectiveness of the target-tracking algorithm has been proved as the target gets detected, irrespective of size of the target, perspective view and cluttered environment.

scholarly journals Recognizing Facial Expression using PCA and Genetic Algorithm

2013 ◽  
pp. 117-120
Author(s):  
Mahima Agrawal ◽  
Shubangi. D. Giripunje ◽  
P. R. Bajaj
Keyword(s):  
Genetic Algorithm ◽  
Facial Expression ◽  
Facial Expressions ◽  
Recognition System ◽  
Video Sequences ◽  
Computational Time ◽  
Expression Recognition ◽  
Research Subject ◽  
Still Images ◽  
Recognition Of Facial Expressions

This paper presents an efficient method of recognition of facial expressions in a video. The works proposes highly efficient facial expression recognition system using PCA optimized by Genetic Algorithm .Reduced computational time and comparable efficiency in terms of its ability to recognize correctly are the benchmarks of this work. Video sequences contain more information than still images hence are in the research subject now-a-days and have much more activities during the expression actions. We use PCA, a statistical method to reduce the dimensionality and are used to extract features with the help of covariance analysis to generate Eigen –components of the images. The Eigen-components as a feature input is optimized by Genetic algorithm to reduce the computation cost.

Pedestrian tracking in FLIR imagery based on modified intensity variation function

2015 ◽  
Vol 23 (10) ◽  
pp. 2980-2988
Author(s):  
郑超 ZHENG Chao ◽  
陈杰 CHEN Jie ◽  
陶会峰 TAO Hui-feng ◽  
殷松峰 YIN Song-feng ◽  
杨星 YANG Xing ◽  
...  
Keyword(s):  
Intensity Variation ◽  
Pedestrian Tracking ◽  
Variation Function

scholarly journals Multitarget Tracking of Pedestrians in Video Sequences Based on Particle Filters

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Hui Li ◽  
Shengwu Xiong ◽  
Pengfei Duan ◽  
Xiangzhen Kong
Keyword(s):  
Target Tracking ◽  
Particle Filters ◽  
Likelihood Function ◽  
Video Sequences ◽  
Appearance Model ◽  
Multitarget Tracking ◽  
Estimation Problems ◽  
Controlled Environments ◽  
Non Gaussian ◽  
Gaussian Estimation

Video target tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in target tracking for nonlinear and non-Gaussian estimation problems. Although most existing algorithms are able to track targets well in controlled environments, it is often difficult to achieve automated and robust tracking of pedestrians in video sequences if there are various changes in target appearance or surrounding illumination. To surmount these difficulties, this paper presents multitarget tracking of pedestrians in video sequences based on particle filters. In order to improve the efficiency and accuracy of the detection, the algorithm firstly obtains target regions in training frames by combining the methods of background subtraction and Histogram of Oriented Gradient (HOG) and then establishes discriminative appearance model by generating patches and constructing codebooks using superpixel and Local Binary Pattern (LBP) features in those target regions. During the process of tracking, the algorithm uses the similarity between candidates and codebooks as observation likelihood function and processes severe occlusion condition to prevent drift and loss phenomenon caused by target occlusion. Experimental results demonstrate that our algorithm improves the tracking performance in complicated real scenarios.

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