Roadside camera motion detection for automated speed measurement

2003 ◽  
Author(s):  
S. Pumrin ◽  
D.J. Dailey
Keyword(s):  
Motion Detection ◽  
Camera Motion ◽  
Speed Measurement

MOVING CAMERA MOVING OBJECT SEGMENTATION IN COMPRESSED VIDEO SEQUENCES

2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI
Keyword(s):  
Motion Detection ◽  
Object Segmentation ◽  
Object Motion ◽  
Moving Object ◽  
Transformation Model ◽  
Video Sequences ◽  
Compressed Domain ◽  
Camera Motion ◽  
Consistency Check ◽  
Moving Object Segmentation

In this paper, we address the problem of camera and object motion detection in the compressed domain. The estimation of camera motion and the moving object segmentation have been widely stated in a variety of context for video analysis, due to their capabilities of providing essential clues for interpreting the high-level semantics of video sequences. A novel compressed domain motion estimation and segmentation scheme is presented and applied in this paper. MPEG-2 compressed domain information, namely Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients, is filtered and manipulated to obtain a dense and reliable Motion Vector Field (MVF) over consecutive frames. An iterative segmentation scheme based upon the generalized affine transformation model is exploited to effect the global camera motion detection. The foreground spatiotemporal objects are separated from the background using the temporal consistency check to the output of the iterative segmentation. This consistency check process can coalesce the resulting foreground blocks and weed out unqualified blocks. Illustrative examples are provided to demonstrate the efficacy of the proposed approach.

scholarly journals Camera Motion Detection using Video Mosaicing

2006 ◽  
Author(s):  
Masaki Naito ◽  
Kazunori Matsumoto ◽  
Keiichiro Hoashi ◽  
Fumiaki Sugaya
Keyword(s):  
Motion Detection ◽  
Camera Motion ◽  
Video Mosaicing

Camera Motion Detection Through Frame Splitting and Combination of Region-Based Motion Signals

2018 ◽  
Vol 32 (09) ◽  
pp. 1855015
Author(s):  
Antonis Ioannidis ◽  
Vasileios Chasanis ◽  
Aristidis Likas
Keyword(s):  
Optical Flow ◽  
Motion Detection ◽  
Video Sequence ◽  
Moving Objects ◽  
Flow Analysis ◽  
Decision Rules ◽  
Camera Motion ◽  
Affine Motion ◽  
Flow Estimation ◽  
The Cost

Most of the existing approaches for camera motion detection are based on optical flow analysis and the use of the affine motion model. However, these methods are computationally expensive due to the cost of optical flow estimation and may be inefficient in the presence of moving objects whose motion is independent of the camera motion. We present an effective approach to detect camera motions by considering four trapezoidal regions in each frame and computing the horizontal and vertical translations of those regions. Then, simple decision rules based on the translations of the regions are employed in order to decide for the existence and the type of camera motion in each frame. In this way, three signals are constructed (pan, tilt, zoom) which are subsequently filtered to improve the robustness of the method. Comparative experiments on a variety of videos indicate that our method efficiently detects any type of camera motion (pan, tilt, zoom), even in the case where moving objects exist in the video sequence.

Cooking Motion Detection by Using Kinect

2016 ◽  
Vol 136 (12) ◽  
pp. 1759-1760
Author(s):  
Masao Izumi ◽  
Kenji Hashimoto
Keyword(s):  
Motion Detection
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