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Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 May 2014) | Viewed by 97557

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Prof. Dr. Andrea Sanna

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Dipartimento di Automatica e Informatica, Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino, Italy
Interests: computer graphics; virtual and augmented reality; human-machine interaction
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Prof. Dr. Fabrizio Lamberti

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Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli Abruzzi 24 I-10129 Torino, Italy
Interests: computer graphics; computer vision; image processing; human-computer and human-robot interaction; computer architectures; intelligent systems
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Dear Colleagues,

Detection and tracking of targets in forward looking infrared (FLIR) imagery are challenging tasks. IR sensors often provide low signal-to-noise ratio and heavy background cluttering images. Non-stationary cameras can introduce further challenges, because detection and tracking might make it necessary to properly deal with sensor ego-motion through suitable estimation and compensation techniques. Moreover, further issues are posed by imagery with multiple and possibly moving target and non-target objects, which can blend into the background, change their signature, size, shape, and even overlap during their motion. Finally, specific applications could introduce cumbersome real-time constraints, thus requiring tracking techniques with a reduced computational footprint.

The objective of this Special Issue is to invite high state-of-the-art research contributions, tutorials, and position papers that address the broad challenges faced in analysis and processing of FLIR imagery. Original papers describing completed and unpublished work that are not currently under review by any other journal/magazine/conference/special issue are solicited.

Prof. Dr. Andrea Sanna
Prof. Dr. Fabrizio Lamberti
Guest Editors

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Keywords

video surveillance
tracking and detection of pedestrians
autonomous vehicles
environmental monitoring
ego-motion compensation and background removal techniques
template-matching algorithms
deformable part models
automatic detection, recognition and identification of targets
real-time target tracking in military scenarios
applications and case studies

Published Papers (12 papers)

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Editorial

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627 KiB

Open AccessEditorial

Advances in Target Detection and Tracking in Forward-Looking InfraRed (FLIR) Imagery

by Andrea Sanna and Fabrizio Lamberti

Sensors 2014, 14(11), 20297-20303; https://doi.org/10.3390/s141120297 - 28 Oct 2014

Cited by 25 | Viewed by 6138

Abstract

Here we give context to the Special Issue on “Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery” in Sensors. We start with an introduction to the role of infrared images in today’s vision-based applications, by outlining the specific challenges that characterize detection and tracking in FLIR images. We then illustrate why selected papers have been chosen to represent the domain of interest, by summarizing their main contributions to the state-of-the-art. Lastly, we sum up the main evidence found, and we underline some of the aspects that are worthy of further investigation in future research activities. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

Research

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1158 KiB

Open AccessArticle

Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

by Riad I. Hammoud, Cem S. Sahin, Erik P. Blasch, Bradley J. Rhodes and Tao Wang

Sensors 2014, 14(10), 19843-19860; https://doi.org/10.3390/s141019843 - 22 Oct 2014

Cited by 72 | Viewed by 7659

Abstract

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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3035 KiB

Open AccessArticle

Relevance-Based Template Matching for Tracking Targets in FLIR Imagery

by Gianluca Paravati and Stefano Esposito

Sensors 2014, 14(8), 14106-14130; https://doi.org/10.3390/s140814106 - 04 Aug 2014

Cited by 10 | Viewed by 6145

Abstract

One of the main challenges in automatic target tracking applications is represented by the need to maintain a low computational footprint, especially when dealing with real-time scenarios and the limited resources of embedded environments. In this context, significant results can be obtained by using forward-looking infrared sensors capable of providing distinctive features for targets of interest. In fact, due to their nature, forward-looking infrared (FLIR) images lend themselves to being used with extremely small footprint techniques based on the extraction of target intensity profiles. This work proposes a method for increasing the computational efficiency of template-based target tracking algorithms. In particular, the speed of the algorithm is improved by using a dynamic threshold that narrows the number of computations, thus reducing both execution time and resources usage. The proposed approach has been tested on several datasets, and it has been compared to several target tracking techniques. Gathered results, both in terms of theoretical analysis and experimental data, showed that the proposed approach is able to achieve the same robustness of reference algorithms by reducing the number of operations needed and the processing time. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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9574 KiB

Open AccessArticle

Automated Detection and Recognition of Wildlife Using Thermal Cameras

by Peter Christiansen, Kim Arild Steen, Rasmus Nyholm Jørgensen and Henrik Karstoft

Sensors 2014, 14(8), 13778-13793; https://doi.org/10.3390/s140813778 - 30 Jul 2014

Cited by 117 | Viewed by 13144

Abstract

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds of agricultural machinery. Detection and recognition of wildlife within the agricultural fields is important to reduce wildlife mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes to the automated detection and classification of animals in thermal imaging. The methods and results are based on top-view images taken manually from a lift to motivate work towards unmanned aerial vehicle-based detection and recognition. Hot objects are detected based on a threshold dynamically adjusted to each frame. For the classification of animals, we propose a novel thermal feature extraction algorithm. For each detected object, a thermal signature is calculated using morphological operations. The thermal signature describes heat characteristics of objects and is partly invariant to translation, rotation, scale and posture. The discrete cosine transform (DCT) is used to parameterize the thermal signature and, thereby, calculate a feature vector, which is used for subsequent classification. Using a k-nearest-neighbor (kNN) classifier, animals are discriminated from non-animals with a balanced classification accuracy of 84.7% in an altitude range of 3–10 m and an accuracy of 75.2% for an altitude range of 10–20 m. To incorporate temporal information in the classification, a tracking algorithm is proposed. Using temporal information improves the balanced classification accuracy to 93.3% in an altitude range 3–10 of meters and 77.7% in an altitude range of 10–20 m Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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910 KiB

Open AccessArticle

Thermal Tracking of Sports Players

by Rikke Gade and Thomas B. Moeslund

Sensors 2014, 14(8), 13679-13691; https://doi.org/10.3390/s140813679 - 29 Jul 2014

Cited by 19 | Viewed by 6503

Abstract

We present here a real-time tracking algorithm for thermal video from a sports game. Robust detection of people includes routines for handling occlusions and noise before tracking each detected person with a Kalman filter. This online tracking algorithm is compared with a state-of-the-art offline multi-target tracking algorithm. Experiments are performed on a manually annotated 2-minutes video sequence of a real soccer game. The Kalman filter shows a very promising result on this rather challenging sequence with a tracking accuracy above 70% and is superior compared with the offline tracking approach. Furthermore, the combined detection and tracking algorithm runs in real time at 33 fps, even with large image sizes of 1920 × 480 pixels. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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6123 KiB

Open AccessArticle

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

by Sungho Kim and Joohyoung Lee

Sensors 2014, 14(7), 13210-13242; https://doi.org/10.3390/s140713210 - 22 Jul 2014

Cited by 58 | Viewed by 10785

Abstract

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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6942 KiB

Open AccessArticle

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

by Xin Li, Rui Guo and Chao Chen

Sensors 2014, 14(6), 11245-11259; https://doi.org/10.3390/s140611245 - 24 Jun 2014

Cited by 19 | Viewed by 7120

Abstract

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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2692 KiB

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Joint Target Tracking, Recognition and Segmentation for Infrared Imagery Using a Shape Manifold-Based Level Set

by Jiulu Gong, Guoliang Fan, Liangjiang Yu, Joseph P. Havlicek, Derong Chen and Ningjun Fan

Sensors 2014, 14(6), 10124-10145; https://doi.org/10.3390/s140610124 - 10 Jun 2014

Cited by 17 | Viewed by 7817

Abstract

We propose a new integrated target tracking, recognition and segmentation algorithm, called ATR-Seg, for infrared imagery. ATR-Seg is formulated in a probabilistic shape-aware level set framework that incorporates a joint view-identity manifold (JVIM) for target shape modeling. As a shape generative model, JVIM features a unified manifold structure in the latent space that is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. In the ATR-Seg algorithm, the ATR problem formulated as a sequential level-set optimization process over the latent space of JVIM, so that tracking and recognition can be jointly optimized via implicit shape matching where target segmentation is achieved as a by-product without any pre-processing or feature extraction. Experimental results on the recently released SENSIAC ATR database demonstrate the advantages and effectiveness of ATR-Seg over two recent ATR algorithms that involve explicit shape matching. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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1314 KiB

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Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

by Zheng-Zhou Li, Jing Chen, Qian Hou, Hong-Xia Fu, Zhen Dai, Gang Jin, Ru-Zhang Li and Chang-Ju Liu

Sensors 2014, 14(6), 9451-9470; https://doi.org/10.3390/s140609451 - 27 May 2014

Cited by 48 | Viewed by 7037

Abstract

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn’t be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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420 KiB

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Thermal-Infrared Pedestrian ROI Extraction through Thermal and Motion Information Fusion

by Antonio Fernández-Caballero, María T. López and Juan Serrano-Cuerda

Sensors 2014, 14(4), 6666-6676; https://doi.org/10.3390/s140406666 - 10 Apr 2014

Cited by 34 | Viewed by 9076

Abstract

This paper investigates the robustness of a new thermal-infrared pedestrian detection system under different outdoor environmental conditions. In first place the algorithm for pedestrian ROI extraction in thermal-infrared video based on both thermal and motion information is introduced. Then, the evaluation of the proposal is detailed after describing the complete thermal and motion information fusion. In this sense, the environment chosen for evaluation is described, and the twelve test sequences are specified. For each of the sequences captured from a forward-looking infrared FLIR A-320 camera, the paper explains the weather and light conditions under which it was captured. The results allow us to draw firm conclusions about the conditions under which it can be affirmed that it is efficient to use our thermal-infrared proposal to robustly extract human ROIs. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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665 KiB

Open AccessArticle

Feature Point Descriptors: Infrared and Visible Spectra

by Pablo Ricaurte, Carmen Chilán, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla and Angel D. Sappa

Sensors 2014, 14(2), 3690-3701; https://doi.org/10.3390/s140203690 - 21 Feb 2014

Cited by 39 | Viewed by 8892

Abstract

This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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Review

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Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

by Mohammad S. Alam and Sharif M. A. Bhuiyan

Sensors 2014, 14(8), 13437-13475; https://doi.org/10.3390/s140813437 - 24 Jul 2014

Cited by 9 | Viewed by 5772

Abstract

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. Full article

(This article belongs to the Special Issue Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery)

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