Object Motion Detection Methods for Real-Time Video Surveillance: A Survey with Empirical Evaluation

Singh, Surender; Prasad, Anand R.; Srivastava, Kingshuk; Bhattacharya, S. D.

doi:10.1007/978-981-13-8406-6_63

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…A motion detection algorithm identifies pixels with motion changes in continuous images, and it usually includes background subtraction, temporary differences, optical flows, and so on [28].…”

Section: Motion Detectionmentioning

confidence: 99%

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

Yang,

Zong,

Zhu

et al. 2023

Water

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The real-time derivation of the concentration, area, and velocity of river surface ice based on camera imagery is essential for predicting the potential risks related to ice blockages in water routes. The key lies in the continuous tracking and velocity measuring of river ice, and reliable ice motion detection is a prerequisite for the dynamic perception of tracking targets. Previous studies did not utilize motion tracking for measuring ice velocity, and particle image velocimetry and feature point matching were used. This study aimed to use deep learning methods to address the challenging problems of deriving the ice concentration, area, and velocity based on camera imagery, and the focus was on measuring the ice velocity and drawing trajectories using the particle video tracking algorithm. We built a dataset named IPC_RI_IDS and collected information during the ice cover break-up process in the Nenjiang River (China). Our suggested approach was divided into four steps: (1) image preprocessing, where the camera image was calibrated to real-world coordinates; (2) determining the ice and water pixels in the camera image using the lightweight semantic segmentation network and then calculating the ice concentration and area; (3) enhancing and optimizing motion detection using the semantic segmentation results; and (4) adapting the particle video tracking algorithm to measure ice velocity using the proposed tracking points generation strategy. Finally, we analyzed the surface ice data in the study area and attempted to predict the stage of the ice break-up process to provide support for the real-time short-term forecasts of ice floods.

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Section: Motion Detectionmentioning

confidence: 99%

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

Yang,

Zong,

Zhu

et al. 2023

Water

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“…Hence, instance segmentation can be considered as a more elaborate implementation of semantic segmentation. Since all the computer vision tasks are similar, in this work mainly object detection and instance segmentation techniques will be examined, as they are the most dominant techniques required in extensive applications such as autonomous driving [68], video surveillance [97], face recognition [105], and robot navigation [117].…”

Section: A Computer Vision Tasksmentioning

confidence: 99%

Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

et al. 2022

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Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic analysis of the existing methods, the challenges related to computer vision tasks are presented. In order to address the existing challenges, a hybrid framework is proposed, where deep learning methods are coupled with domain knowledge. An additional feature of this survey is that a review of the currently existing approaches integrating domain knowledge with deep learning techniques is presented. Finally, some conclusions on the implementation of hybrid architectures to perform computer vision tasks are discussed.

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Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case study

Kumar,

Singh

2023

Multimed Tools Appl

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Object Motion Detection Methods for Real-Time Video Surveillance: A Survey with Empirical Evaluation

Cited by 4 publications

References 29 publications

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity

Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case study

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