Motion detection using block based bi-directional optical flow method

[Display omitted] •Optical flow based moving object detection algorithm is proposed.•Bi-directional optical flow field is used for motion estimation and detection.•A histogram and plot based thresholding scheme is employed for motion detection.•Foreground is detected using morphological operation, c...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of visual communication and image representation Ročník 49; s. 89 - 103
Hlavní autoři: Sengar, Sandeep Singh, Mukhopadhyay, Susanta
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.11.2017
Témata:
Block
Morphology
Motion detection
Normalization
Optical flow
Block
Motion detection
Normalization
Morphology
Optical flow
ISSN:1047-3203, 1095-9076
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:[Display omitted] •Optical flow based moving object detection algorithm is proposed.•Bi-directional optical flow field is used for motion estimation and detection.•A histogram and plot based thresholding scheme is employed for motion detection.•Foreground is detected using morphological operation, connected component analysis.•Our technique is compared with existing methods using real video datasets. Detecting moving objects from video frame sequences has a lot of useful applications in computer vision. This proposed method of moving object detection first estimates the bi-directional optical flow fields between (i) the current frame and the previous frame and between (ii) the current frame and the next frame. The bi-directional optical flow field is then subjected to normalization and enhancement. Each normalized and enhanced optical flow field is then divided into non-overlapping blocks. The moving objects are finally detected in the form of binary blobs by examining the histogram based thresholded values of such optical flow field of each block as well as the optical flow field of the candidate flow value. Our technique has been conceptualized, implemented and tested on real video data sets with complex background environment. The experimental results and quantitative evaluation establish that our technique achieves effective and efficient results than other existing methods.
ISSN:1047-3203
1095-9076
DOI:10.1016/j.jvcir.2017.08.007