Moving Object Detection by 3D Flow Field Analysis

Jiang, Cansen; Paudel, Danda Pani; Fofi, David; Fougerolle, Yohan; Demonceaux, Cédric

doi:10.1109/tits.2021.3055766

Cited by 15 publications

(6 citation statements)

References 80 publications

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“…As a result, graph-based analyzing and processing methods, such as spectral clustering and graph-based filtering, are popular ways of processing dynamic point clouds. The authors in [30] utilize spectral clustering to separate the motion flows in their proposed object detection and extraction framework on mobile lidar data. A robust dynamic point cloud segmentation routine is proposed in [31], where the spectral clustering is used to generate the initial segmentation.…”

Section: B Real Datasetsmentioning

confidence: 99%

Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

Deng,

Zhang,

et al. 2024

Preprint

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Section: B Real Datasetsmentioning

confidence: 99%

Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

Deng,

Zhang,

et al. 2024

Preprint

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“…The majority of existing research for DL-based AD deals with captured data from fixed cameras, while AD from moving cameras is still a limited domain. Thus, this field is a promising area of study [105]. Future work will require more effort to be put into the collection of data and the development of algorithms for AD in moving cameras.…”

Section: Ad From Moving Camerasmentioning

confidence: 99%

Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

et al. 2022

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In the last few years, due to the continuous advancement of technology, human behavior detection and recognition have become important scientific research in the field of computer vision (CV). However, one of the most challenging problems in CV is anomaly detection (AD) because of the complex environment and the difficulty in extracting a particular feature that correlates with a particular event. As the number of cameras monitoring a given area increases, it will become vital to have systems capable of learning from the vast amounts of available data to identify any potential suspicious behavior. Then, the introduction of deep learning (DL) has brought new development directions for AD. In particular, DL models such as convolution neural networks (CNNs) and recurrent neural networks (RNNs) have achieved excellent performance dealing with AD tasks, as well as other challenging domains like image classification, object detection, and speech processing. In this review, we aim to present a comprehensive overview of those research methods using DL to address the AD problem. Firstly, different classifications of anomalies are introduced, and then the DL methods and architectures used for video AD are discussed and analyzed, respectively. The revised contributions have been categorized by the network type, architecture model, datasets, and performance metrics that are used to evaluate these methodologies. Moreover, several applications of video AD have been discussed. Finally, we outlined the challenges and future directions for further research in the field.

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“…Much less preprocessing is required in the convolutional neural network algorithm compared to other related technologies, such as training and learning (Pradhan et al, 2017). R02 used three-phase flow field analysis, which is a framework consisting of a series of unordered and uncoordinated point clouds to detect and extract a moving object in a video (Jiang et al, 2021). In R04, K-mean clustering is proposed which is an unsupervised segmentation algorithm that can identify clusters in an image based on the similarity of data within the same group (Xie et al, 2021).…”

Section: Comparative Studymentioning

confidence: 99%

A comparative study of intelligent surveillance with big data: methods, strengths, weaknesses, and future directions

Habeeb

Abdulkhudhur

Habeeb

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Various challenges in intelligent surveillance with big data have attracted researchers to propose and design methods and techniques to solve these challenges. These methods have become an important part of healthcare, education, travel, security, and geographic research in our cities. They can monitor vehicles, count people, predict the intensity of tropical cyclones and give advance warnings of erratic behaviors or actions. However, the expansion of complex scenarios and big video data produces many papers describing how this data can be processed, analyzed, saved, and retrieved faster. The progress of these new trends is insufficient for intelligent surveillance solutions with big data. Hence, this research covers the academic literature with a comparative study of intelligent surveillance methods with big data. The study identified 98 papers for review from 2017 to 2022, then only 22 of them were finally selected for comparison according to a set of inclusion criteria. The result obtained includes all major aspects of these methods, such as their methodologies, purposes, strengths, and future directions, over the past six years of research. The objective of this paper was achieved by practically investigating recent trends in intelligent surveillance and its sub-topics in our life, using big data analytics. I hope that this comparative study will be a challenge for researchers and graduate students to find the gaps faster and overcome the drawbacks of the current intelligent surveillance methods using complex scenarios.

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Moving Object Detection by 3D Flow Field Analysis

Cited by 15 publications

References 80 publications

Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach

Review on Deep Learning Approaches for Anomaly Event Detection in Video Surveillance

A comparative study of intelligent surveillance with big data: methods, strengths, weaknesses, and future directions

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