Moving Object Detection for a Moving Camera Based on Global Motion Compensation and Adaptive Background Model

Yu, Yang; Kurnianggoro, Laksono; Jo, Kang-Hyun

doi:10.1007/s12555-018-0234-3

Cited by 41 publications

(27 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, in case of moving camera, most of the commonly known methods are adaptations of background subtraction concept [10]. To deal with this challenge, certain works [20][21][22][23][24] opted for selecting points from a grid then using an optical flow or several tracking approaches such as Kanade-Lucas-Tomasi (KLT) feature tracker. Zhao et al [23] suggested a new framework named IFB (Integration of Foreground and Background cues).…”

Section: Camera Motionmentioning

confidence: 99%

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Lejmi¹,

Khalifa²,

Mahjoub³

2020

View full text Add to dashboard Cite

In the current era, the implementation of automated security video surveillance systems is particularly needy in terms of human violence recognition. Nevertheless, the latter encounters various interlinked difficulties which require efficient solutions as well as feasible methods that provide a relevant distinction between normal human actions and abnormal ones. In this paper, we present an overview of these issues and a literature review of the related works and current research on-going efforts on this field and suggests a novel prediction model for violence recognition, based on a preliminary spatio-temporal features extraction using the material derivative which describes the rate of change of a particle while in motion with respect to time. The classification algorithm is then carried out using a deep learning LSTM technique to classify generated features into eight specified violent and non-violent categories and a prediction value for each class of action is calculated. The whole model is trained on a public dataset and its classification capacity is evaluated on a confusion matrix which assembles all the predictions made by the system with their actual labels. The obtained results are promising and show that the proposed model can be potentially useful for detecting human violence.

show abstract

Section: Camera Motionmentioning

confidence: 99%

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Lejmi¹,

Khalifa²,

Mahjoub³

2020

View full text Add to dashboard Cite

show abstract

“…Lenac et al [10] suggested a moving object detection method using dense optical flow generated by moving thermal camera, whereby rotational movement is compensated with the IMU sensor. Yu et al [11] proposed a method estimating the global motion (egomotion) with grid-based key points by optical flow. An expectation maximization framework was used by Liu et al to detect the foreground region [12].…”

Section: Related Workmentioning

confidence: 99%

Moving Object Detection from Moving Camera Image Sequences Using an Inertial Measurement Unit Sensor

et al. 2019

View full text Add to dashboard Cite

This paper describes a new method for the detection of moving objects from moving camera image sequences using an inertial measurement unit (IMU) sensor. Motion detection systems with vision sensors have become a global research subject recently. However, detecting moving objects from a moving camera is a difficult task because of egomotion. In the proposed method, the interesting points are extracted by a Harris detector, and the background and foreground are classified by epipolar geometry. In this procedure, an IMU sensor is used to calculate the initial fundamental matrix. After the feature point classification, a transformation matrix is obtained from matching background feature points. Image registration is then applied to the consecutive images, and a difference map is extracted to find the foreground region. Finally, a minimum bounding box is applied to mark the detected moving object. The proposed method is implemented and tested with numerous real-world driving videos, which show that it outperforms the previous work. Appl. Sci. 2020, 10, 268 3 of 13 Appl. Sci. 2020, 10, x 3 of 12 Appl. Sci. 2020, 10, 268 13 of 13 34. Mirzaei, F.M.; Roumeliotis, S.I. A Kalman filter-based algorithm for IMU-camera calibration: Observability analysis and performance evaluation.

show abstract

“…At present, the methods for moving object detection mainly contain background subtraction method [1], [28], optical flow method [2], [27], frame difference method [3]. The background subtraction method has been intensively investigated.…”

Section: Introductionmentioning

confidence: 99%

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Zhang

Li³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.

show abstract

Moving Object Detection for a Moving Camera Based on Global Motion Compensation and Adaptive Background Model

Cited by 41 publications

References 28 publications

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

A Novel Spatio-Temporal Violence Classification Framework Based on Material Derivative and LSTM Neural Network

Moving Object Detection from Moving Camera Image Sequences Using an Inertial Measurement Unit Sensor

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Contact Info

Product

Resources

About