Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction

Wu, Yuanyuan; He, Xiaohai; Nguyen, Truong Q.

doi:10.1109/tcsvt.2015.2493499

Cited by 72 publications

(44 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here, the algorithms exploited motion estimation based on background subtraction and color probability analysis to detect candidate smoke blobs. Some of the techniques seen above [14][15][16][17]21,[29][30][31] perform basically a background subtraction, so they just want to recognize the foreground. Other papers, like [19], implement more sophisticated video processing techniques, like Kalman based motion estimation, but with fixed parameters not aware of the specific camera configuration.…”

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

Gagliardi

Saponara

2020

Energies

View full text Add to dashboard Cite

This paper proposes a video-based smoke detection technique for early warning in antifire surveillance systems. The algorithm is developed to detect the smoke behavior in a restricted video surveillance environment, both indoor (e.g., railway carriage, bus wagon, industrial plant, or home/office) or outdoor (e.g., storage area or parking area). The proposed technique exploits a Kalman estimator, color analysis, image segmentation, blob labeling, geometrical features analysis, and M of N decisor, in order to extract an alarm signal within a strict real-time deadline. This new technique requires just a few seconds to detect fire smoke, and it is 15 times faster compared to the requirements of fire-alarm standards for industrial or transport systems, e.g., the EN50155 standard for onboard train fire-alarm systems. Indeed, the EN50155 considers a response time of at least 60 s for onboard systems. The proposed technique has been tested and compared with state-of-art systems using the open access Firesense dataset developed as an output of a European FP7 project, including several fire/smoke indoor and outdoor scenes. There is an improvement of all the detection metrics (recall, accuracy, F1 score, precision, etc.) when comparing Advanced Video SmokE Detection (AdViSED) with other video-based antifire works recently proposed in literature. The proposed technique is flexible in terms of input camera type and frame size and rate and has been implemented on a low-cost embedded platform to develop a distributed antifire system accessible via web browser.

show abstract

Section: State Of the Art Review Of Video-based Smoke Detection Algormentioning

confidence: 99%

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

Gagliardi

Saponara

2020

Energies

View full text Add to dashboard Cite

show abstract

“…Despite of its complexity, their approach can effectively detect moving object, even when heavy parallax is present among frames of a video sequence. Recently, a background subtraction method based on a coarse-to-fine thresholding scheme was proposed by [134], which has low complexity and is robust against free motion of cameras. In another recent work, Gong et al [135] proposed a background subtraction based on codebook modelling which uses full colour information to detect moving objects, when the camera was mounted on a moving vehicle.…”

Section: Modelling Based Background Subtractionmentioning

confidence: 99%

“…This transformation can be an 2D parameter transform in which the parameters can be adjusted (e.g. using the affine transform for PTZ motions [25] or the perspective transform for free motions [26]). …”

Section: Introductionmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

210

View full text Add to dashboard Cite

This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

show abstract

“…The technique uses entropy and uses saliency map to perform identification of an object. Wu et al [21] have used subtraction mechanism for background using singular value decomposition followed by adaptive thresholding. The technique uses fast in painting for reconstructing an image.…”

Section: Related Techniquesmentioning

confidence: 99%

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

show abstract

Moving Object Detection With a Freely Moving Camera via Background Motion Subtraction

Cited by 72 publications

References 37 publications

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

New trends on moving object detection in video images captured by a moving camera: A survey

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Contact Info

Product

Resources

About