Detection and tracking of sea-surface targets in infrared and visual band videos using the bag-of-features technique with scale-invariant feature transform

Can, Tolga; Karali, A. Onur; Aytaç, Tayfun

doi:10.1364/ao.50.006302

Cited by 21 publications

(15 citation statements)

References 18 publications

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“…Can et al [7] proposed to use Scale Invariant Feature Transform (SIFT) features with the Bag-of-Features (BOF) technique for detection and tracking of sea-surface targets in infrared (IR) and visual band video streams. They used the K-means algorithm to generate clusters in the visual band.…”

Section: Segmentationmentioning

confidence: 99%

“…Other metrics have been used such as the ones presented by Can et al [7] which assessed the performance of tracking algorithm. Although tracking performance is beyond the scope of this research project, some of these metrics could potentially be used  Metric 4 (M4): M4 defined the true positive rate, which is calculated as the ratio between the correctly detected target area and the whole detected target area.…”

Section: Tpr Ppvmentioning

confidence: 99%

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Multispectral image analysis for object recognition and classification

2016

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Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance.The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to "fuse" the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system's ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier.The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.iii Acknowledgment

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Section: Segmentationmentioning

confidence: 99%

Section: Tpr Ppvmentioning

confidence: 99%

Multispectral image analysis for object recognition and classification

2016

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“…2D optical flow is commonly used as a feature in motion-based segmentation and tracking applications (e.g., [21]). Surprisingly, visual-motion research on tracking applications have rarely exploited scene flow information.…”

Section: Scene Flowmentioning

confidence: 99%

Skeleton body pose tracking from efficient three-dimensional motion estimation and volumetric reconstruction

Zhang

Soon

2012

Appl. Opt.

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We address the problem of body pose tracking in a scenario of multiple camera setup with the aim of recovering body motion robustly and accurately. The tracking is performed on three-dimensional (3D) space using 3D data, including colored volume and 3D optical flow, which are reconstructed at each time step. We introduce strategies to compute multiple camera-based 3D optical flow and have attained efficient and robust 3D motion estimation. Body pose estimation starts with a prediction using 3D optical flow and then is changed to a lower-dimensional global optimization problem. Our method utilizes a voxel subject-specific body model, exploits multiple 3D image cues, and incorporates physical constraints into a stochastic particle-based search initialized from the deterministic prediction and stochastic sampling. It leads to a robust 3D pose tracker. Experiments on publicly available sequences show the robustness and accuracy of our approach.

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“…Since the background clutter is extremely complex and strong, the target seems quite small and is easily drowned out by disturbances [4][5][6]. Finally, although a number of algorithms have been presented for small IR target tracking in specific situations, e.g., sea clutter during the last decades, many of these trackers may fail in different circumstances [7,8]. For example, a tracker may be good in coping with sea-wave clutter but has difficulty in handling cluttered sky-cloud situations.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes

Wang

Chen

2015

Appl. Opt.

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Tracking infrared (IR) small targets is a vital component of many computer vision applications, including IR precise guidance, early warning, and IR remote sensing. Various complicated scenes, however, present significant challenges to the tracking task. To solve this problem, we present a novel 3D spatiotemporal difference-of-Gaussians (DoG) filter-based algorithm for tracking small targets in IR videos of various complex scenes. First, biologically inspired 3D DoG filters are proposed for IR small target tracking, which are capable of accounting for spatial and temporal information. Then, based on such filters, an effective and robust tracker is constructed to track the small targets, which are spatiotemporally distinguishable from background clutter. Extensive experiments show that our approach tracks the small targets accurately and robustly in realistic IR videos of complex backgrounds that present unique difficulties to other approaches.

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Detection and tracking of sea-surface targets in infrared and visual band videos using the bag-of-features technique with scale-invariant feature transform

Cited by 21 publications

References 18 publications

Multispectral image analysis for object recognition and classification

Multispectral image analysis for object recognition and classification

Skeleton body pose tracking from efficient three-dimensional motion estimation and volumetric reconstruction

Spatiotemporal difference-of-Gaussians filters for robust infrared small target tracking in various complex scenes

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