Camera Motion Detection in Video Sequences Using Motion Cooccurrences

Author(s):  
Hyun-Ho Jeon ◽  
Andrea Basso ◽  
Peter F. Driessen
2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI

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.


Author(s):  
Masaki Naito ◽  
Kazunori Matsumoto ◽  
Keiichiro Hoashi ◽  
Fumiaki Sugaya

Author(s):  
Stefan Schulte ◽  
Vladimir Zlokolica ◽  
Aleksandra Pizurica ◽  
Wilfried Philips ◽  
Etienne Kerre

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Gao Chunxian ◽  
Zeng Zhe ◽  
Liu Hui

Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.


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