Mean shift based target tracking robust to illuminance variation in infrared image sequences

2014 ◽  
Author(s):  
Hamza Soganci ◽  
Aysun Coban
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Infrared Image ◽  
Image Sequences

scholarly journals A Study of LM-IHPDA Algorithm for Multi-Target Tracking in Infrared Image Sequences

2013 ◽  
Vol 19 (3) ◽  
pp. 209-218 ◽  
Author(s):  
Tae Han Kim ◽  
Byung In Choi ◽  
Ji Eun Kim ◽  
Yu Kyung Yang ◽  
Taek Lyul Song
Keyword(s):  
Target Tracking ◽  
Infrared Image ◽  
Image Sequences ◽  
Multi Target Tracking

scholarly journals Aerial Infrared Target Tracking in Complex Background Based on Combined Tracking and Detecting

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yangguang Hu ◽  
Mingqing Xiao ◽  
Kai Zhang ◽  
Xiaotian Wang
Keyword(s):  
Deep Learning ◽  
Target Tracking ◽  
High Efficiency ◽  
Infrared Image ◽  
Image Sequences ◽  
Correlation Filter ◽  
Weapon Systems ◽  
Complex Background ◽  
Challenging Research ◽  
Comparable Performance

Aerial infrared target tracking is the basis of many weapon systems, especially the air-to-air missile. Till now, it is still challenging research to track the aircraft in the event of complex background. In this paper, we focus on developing an algorithm that could track the aircraft fast and accurately based on infrared image sequence. We proposed a framework composed of a tracker T based on correlation filter and a detector D based on deep learning, which we call combined tracking and detecting (CTAD). With such collaboration, the algorithm enjoys both the high efficiency provided by correlation filter and the strong discriminative power provided by deep learning. Finally, we performed experiments on three representative infrared image sequences and two sequences from VOT-TIR2016 dataset to quantitatively evaluate the performance of our algorithm. To evaluate our algorithm scientifically, we present the experiments performed on two sequences from AMCOM FLIR dataset of the proposed algorithm. The experimental results demonstrate that our algorithm could track the infrared target reliably, which shows comparable performance with the deep tracker, while running at a fast speed of about 18.1 fps.

scholarly journals Infrared Moving Multi-Target Tracking Based on Particle Filter and FCM

2013 ◽  
Vol 347-350 ◽  
pp. 3792-3796
Author(s):  
Fan Jun Hu ◽  
Wen Jun Shi
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Median Filter ◽  
Infrared Image ◽  
Image Sequences ◽  
Fcm Algorithm ◽  
Efficient Approach ◽  
Morphological Filtering ◽  
Multi Target Tracking ◽  
Island Shore

An efficient approach based on particle filter and FCM is presented to realize moving infrared multi-target tracking under Island shore background. Some possible targets can be obtained and saved by processing IR data through denoising by median filter, extracting edge, identifying and eliminating sea-sky line, morphological filtering and etc. Data association and robust multi-target tracking can be realized by the proposed particle filter and FCM algorithm. The proposed approach is validated to track multi-target effectively by using actual infrared image sequences with Island shore background. Experiment results indicate the feasibility and effectiveness of the proposed method.

Algorithm of target tracking based on Mean-Shift with adaptive bandwidth of kernel function

2009 ◽  
Vol 29 (6) ◽  
pp. 1680-1682
Author(s):  
Chang-tao CHEN ◽  
Qin ZHU ◽  
Sheng-yi ZHOU ◽  
Jia-ming ZHANG
Keyword(s):  
Target Tracking ◽  
Kernel Function ◽  
Mean Shift ◽  
Adaptive Bandwidth
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