ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

Mandal, Murari; Chaudhary, Mallika; Vipparthi, Santosh Kumar; Murala, Subrahmanyam; Gonde, Anil Balaji; Nagar, Shyam Krishna

doi:10.1049/iet-cvi.2018.5206

Cited by 21 publications

(11 citation statements)

References 66 publications

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“…6). The traditional methods [28], [28], [31], [31], [32], [32], [33], [51], [56], [57], [64], [88] address these diverse challenges by feature extraction, background Fig. 2: The recent evolution of change detection performance with deep learning approaches in CDnet 2014 dataset [34].…”

Section: A Challenges and Issuesmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Section: A Challenges and Issuesmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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“…Advancement in deep learning algorithms and the availability of largescale labeled datasets has fueled the progress in important applications in several domains. Some of the low-level tasks in computer vision include image classification [10,20,21,55], object detection [31-33, 36, 53, 73], semantic segmentation [7,19], video object segmentation [44,51,69], motion detection [1,38,39,41,48,62,71] and visual tracking [2,15,61]. Although many challenging applications are presented to the researchers in UAV based computer vision community.…”

Section: Mor-uav Dataset 21 Comparison With Existing Uav Datasetsmentioning

confidence: 99%

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Mandal

Kumar

Vipparthi

2020

Proceedings of the 28th ACM International Conference on Multimedia

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Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR)? The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesnâĂŹt require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging.

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“…St-Charles et al [4,5] proposed a more sophisticated algorithm SuBSENSE using spatiotemporal feature descriptors LBSP and adaptive feedback mechanism. Various adaptations of SuBSENSE [11,12] have also been proposed to further improve the performance. A deterministic background model update policy was proposed by Mandal et al [10].…”

Section: Introductionmentioning

confidence: 99%

3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection

Mandal

Dhar

Mishra

et al. 2019

IEEE Signal Process. Lett.

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In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection. The 3DFR framework consists of three feature streams: a swift 3D feature reductionist stream (AvFeat), a contemporary feature stream (ConFeat) and a temporal median feature map. These multilateral foreground/background features are further refined through an encoder-decoder network. As a result, the proposed framework not only detects temporal changes but also learns high-level appearance features. Thus, it incorporates the object semantics for effective change detection. Furthermore, the proposed framework is validated through a scene independent evaluation scheme in order to demonstrate the robustness and generalization capability of the network. The performance of the proposed method is evaluated on the benchmark CDnet 2014 dataset. The experimental results show that the proposed 3DFR network outperforms the state-of-the-art approaches.

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ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

Cited by 21 publications

References 66 publications

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

3DFR: A Swift 3D Feature Reductionist Framework for Scene Independent Change Detection

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