scholarly journals Two Streams Multiple-Model Object Tracker for Thermal Infrared Video

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32383-32392 ◽  
Author(s):  
Mohd Asyraf Zulkifley
Keyword(s):  
Thermal Infrared ◽  
Multiple Model ◽  
Model Object ◽  
Infrared Video

Activity recognition in thermal infrared video

2015 ◽  
Author(s):  
Jakir Hossen ◽  
Eddie L Jacobs ◽  
Fahmida Kishowara Chowdhury
Keyword(s):  
Activity Recognition ◽  
Thermal Infrared ◽  
Infrared Video

Thermal infrared video details multiscale groundwater discharge to surface water through macropores and peat pipes

2016 ◽  
Vol 30 (14) ◽  
pp. 2510-2511 ◽  
Author(s):  
Martin A. Briggs ◽  
Danielle K. Hare ◽  
David F. Boutt ◽  
Glorianna Davenport ◽  
John W. Lane
Keyword(s):  
Surface Water ◽  
Groundwater Discharge ◽  
Thermal Infrared ◽  
Infrared Video

scholarly journals Detection and Tracking of Moving Targets for Thermal Infrared Video Sequences

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3944 ◽  
Author(s):  
Chenming Li ◽  
Wenguang Wang
Keyword(s):  
Background Subtraction ◽  
Single Point ◽  
Thermal Infrared ◽  
Selection Strategy ◽  
Probability Map ◽  
Detection And Tracking ◽  
Target Track ◽  
Infrared Video ◽  
Random Finite Sets ◽  
Track Before Detect

The joint detection and tracking of multiple targets from raw thermal infrared (TIR) image observations plays a significant role in the video surveillance field, and it has extensive applied foreground and practical value. In this paper, a novel multiple-target track-before-detect (TBD) method, which is based on background subtraction within the framework of labeled random finite sets (RFS) is presented. First, a background subtraction method based on a random selection strategy is exploited to obtain the foreground probability map from a TIR sequence. Second, in the foreground probability map, the probability of each pixel belonging to a target is calculated by non-overlapping multi-target likelihood. Finally, a δ generalized labeled multi-Bernoulli ( δ -GLMB) filter is employed to produce the states of multi-target along with their labels. Unlike other RFS-based filters, the proposed approach describes the target state by a pixel set instead of a single point. To meet the requirement of factual application, some extra procedures, including pixel sampling and update, target merging and splitting, and new birth target initialization, are incorporated into the algorithm. The experimental results show that the proposed method performs better in multi-target detection than six compared methods. Also, the method is effective for the continuous tracking of multi-targets.

scholarly journals ON MATHEMATICAL MODELLING OF SYNTHETIC MEASURES

2018 ◽  
Vol 23 (4) ◽  
pp. 699-711 ◽  
Author(s):  
Boleslaw Borkowski ◽  
Zbigniew Binderman ◽  
Ryszard Kozera ◽  
Alexander Prokopenya ◽  
Wieslaw Szczesny
Keyword(s):  
Mathematical Modelling ◽  
Necessary Conditions ◽  
Multidimensional Analysis ◽  
Multiple Model ◽  
Model Object

This work deals with some properties of synthetic measures designed to differentiate objects in a multidimensional analysis. The aggregate synthetic measures are discussed here to rank the objects including those validating the concentration spread. The paper shows that currently used various measures (based either on a single or a multiple model object) do not satisfy the necessary conditions requested to be met by a "good" synthetic measure.

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