Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Chen, Shuoyang; Xu, Tingfa; Li, Daqun; Zhang, Jizhou; Jiang, Shenwang

doi:10.3390/s16101758

Cited by 13 publications

(8 citation statements)

References 49 publications

(53 reference statements)

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“…6 shows example video frames and their associated ground truths from BMC dataset. Some examples of works using this dataset to evaluate their background subtraction algorithms are [19], [109], [110].…”

Section: ) Bmc Datasetmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Yan-Jun Liu. result in false detections under complex situations [19]. A quick search for background subtraction on IEEE Xplore shows 2430 publications in the last ten years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: ) Bmc Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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“…The approaches of moving object detection by cameras are [12], [13], [14]. They indicate how to detect moving objects from camera images.…”

Section: Related Workmentioning

confidence: 99%

In-Vehicle Camera Images Prediction by Generative Adversarial Network

Watanabe¹,

Gonsalves²

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

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Moving object detection is one of the fundamental technologies necessary to realize autonomous driving. In this study, we propose the prediction of an in-vehicle camera image by Generative Adversarial Network (GAN). From the past images input to the system, it predicts the future images at the output. By predicting the motion of a moving object, it can predict the destination of the moving object. The proposed model can predict the motion of moving objects such as cars, bicycles, and pedestrians.

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“…Detection, tracking, and positioning physical movements of non‐rigid objects play an important role in intelligent video surveillance system, self‐governing navigation, human‐machine interface application, behaviour or activity analysis, the positioning of the vehicle, fire, etc . As activities such as crime, terrorism, and other hideous or suspicious events can occur 24x7, it is a constraint for a human to remain vigilant in both manually attended and unattended environments.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of colour texture‐based multiple object detection using morphological gradient approach

Kandavalli

Lincon

2018

Concurrency and Computation

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Summary Non‐rigid moving multiple objects detection and tracking play an important role in intelligent video surveillance system, autonomous navigation, and activity analysis. Closed Circuit Television (CCTV) systems are deployed in numerous areas such as airports, traffic intersections, underground stations, mass events, mall, schools, and organisations for security and public surveillance. Although these cameras record continuous video 24x7, it is a human constraint to manually monitor all events such as crime, terrorism, hideous, suspicious activities, the positioning of the vehicle, and fire recorded from a number of cameras. Moreover, problems like dynamic background, the creation of ghost, sensor noise, varying illumination, and colour and compression artefacts affect effective detection of multiple moving objects. This study presents an effective approach named as enhanced Fractal Texture Analysis with KNN classifier (FTAKC) for tracking and detection of multiple objects from a video sequence. The proposed approach comprises three main phases, namely, detection of moving object, tracking of the object (enhanced Fractal Texture Analysis), and behaviour analysis for activity recognition (KNN classifier). The image feature has been extracted based on colour, texture, and geometry were used to identify and track multiple objects in video frames, and Problem domain knowledge rules were applied to distinguish normal or anomalous activities as well as behaviours. Edge detection algorithm (Intersection over Union (IoU) threshold to determine possible edge connections) was applied toward enhancing the illumination variation by multi‐block Local Binary Pattern (LBP) temporal‐analysis to do the segmentation. Finally, the efficiency and effectiveness of the proposed approach has been estimated based on the measure of average PSNR, precision, recall, f‐measure, accuracy, and execution time. The Laboratory for Image and Media Understanding (LIMU) dataset has been utilised toward illustrating the robustness of the proposed approach. Furthermore, it evaluated the performance based on the measure of precision, recall, and F‐measure metrics. It has been tentatively demonstrated that the proposed approach is suitable for recognizing multiple moving object with detection accuracy up to 93.56%. The simulated results show that suggested approach is robust, flexible, as well as able to outperform the traditional methods than the present object detection method.

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Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Cited by 13 publications

References 49 publications

A Comprehensive Survey of Video Datasets for Background Subtraction

A Comprehensive Survey of Video Datasets for Background Subtraction

In-Vehicle Camera Images Prediction by Generative Adversarial Network

Design and implementation of colour texture‐based multiple object detection using morphological gradient approach

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