Moving target detection in thermal infrared imagery using spatiotemporal information

Akula, Aparna; Ghosh, Ripul; Kumar, Satish; Sardana, Harish Kumar

doi:10.1364/josaa.30.001492

Cited by 28 publications

(6 citation statements)

References 36 publications

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“…In this section, the proposed method is evaluated on real data presented in [50]. This is a thermal video data of pedestrians and vehicles sampled at 10Hz obtained using a fixed camera in an open environment.…”

Section: Real Datamentioning

confidence: 99%

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Aftab

Hostettler

Freitas

et al. 2019

IEEE Trans. Veh. Technol.

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Section: Real Datamentioning

confidence: 99%

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Aftab

Hostettler

Freitas

et al. 2019

IEEE Trans. Veh. Technol.

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“…To illustrate the different stages of the method operation, we used the thermal infrared image "ambassador_morning" from the CSIR-CSIO Moving Object Thermal Infrared Imagery Dataset (MOTIID) [51]. Figure 2 shows the probability distributions calculated for different classes of image data points and the constructed KNN tree.…”

Section: Illustration Of the Methods Stagesmentioning

confidence: 99%

“…The four infrared image sequences of the CSIR-CSIO Moving Object Thermal Infrared Imagery Dataset (MOTIID) data set named "ambassador_morning", "auto_partially_occluded", "bike_far" and "dog_evening" [51].…”

mentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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“…They collected 961 sets of vehicles samples and each set of the sample consists of an original image shot, a remodeled visual image, and an audio clip. CSIR-CSIO Moving Object Thermal Infrared Imagery Dataset (MOTIID) [122] was created in 2013 and contains 18 video files to investigate moving object detection in thermal infrared imagery. This dataset was also used in [123] to evaluate statistical based background subtraction technique in infrared videos.…”

Section: ) Otcbvs Benchmark Datasetsmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Moving target detection in thermal infrared imagery using spatiotemporal information

Cited by 28 publications

References 36 publications

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

A Comprehensive Survey of Video Datasets for Background Subtraction

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