Automatic Detection of Crucial Areas Based on Speed Correlation in Video Sequences

2013 ◽  
Vol 427-429 ◽  
pp. 1789-1793
Author(s):  
Shuang Jun Liu ◽  
Rong Yi Cui
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Observation Time ◽  
Video Sequences ◽  
Video Frame ◽  
Surveillance Video ◽  
Optical Flow Field ◽  
Video Images ◽  
The Difference ◽  
Examination Room

Based on video frame differential optical flow field, a method of crucial area detection for surveillance video images of examination room is proposed in this paper. Firstly, the optical flow field was calculated with the difference between two adjacent frames. Secondly, the scene was divided roughly into several blocks, and the blocks of which centroid speed is higher than given threshold were further divided into fine sub-blocks, and furthermore, the sub-block which has maximum centroid speed in the block was marked as the area of abnormal target. Finally, the sub-blocks with exceptional speed in the same observation time slice were judged to be the correlate areas with abnormal speed (CAAS), and the intersection of adjacent CAAS were determined as the crucial area. Experimental results show that the proposed method can effectively detect the abnormal movement area, and can accurately position the crucial area affecting other targets movement.

The Difference Effect of Wavelet Transforms and Gaussian Filter in Multi-Resolution Optical Flow

2013 ◽  
Vol 475-476 ◽  
pp. 273-277 ◽  
Author(s):  
Zhao Hui Han ◽  
Yan Feng Wang
Keyword(s):  
Wavelet Transform ◽  
Flow Field ◽  
Optical Flow ◽  
Wavelet Transforms ◽  
Flow Distribution ◽  
Field Calculation ◽  
Gaussian Filter ◽  
Optical Flow Method ◽  
Optical Flow Field ◽  
The Difference

Multi-resolution optical flow is the main trends of the current optical flow calculation. Wavelet transform and Gaussian filter are widely used to build multi-resolution structure of the image. In this paper, LK optical flow method mixed with pyramid was used to study the optical flow field. The different effects of wavelet transform and Gaussian filter in the flow distribution was also discussed. Experimental results show that the selected image decomposition method has a significant role in optical flow field calculation.

Dynamic estimation of optical flow field using objective functions

1989 ◽  
Vol 7 (4) ◽  
pp. 259-267 ◽  
Author(s):  
Zhao Wei-Zhao ◽  
Qi Fei-Hu ◽  
Young Tzay Y
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Objective Functions ◽  
Optical Flow Field ◽  
Dynamic Estimation

Motion-Based Template Matching for Obstacle Detection

2004 ◽  
Vol 8 (5) ◽  
pp. 469-476
Author(s):  
Kazuhiko Kawamoto ◽  
◽  
Naoya Ohnishi ◽  
Atsushi Imiya ◽  
Reinhard Klette ◽  
...  
Keyword(s):  
Optical Flow ◽  
Template Matching ◽  
Roc Curves ◽  
Image Sequences ◽  
Obstacle Detection ◽  
Frame Rate ◽  
Video Frame ◽  
Positive Fraction ◽  
Indoor Scenes ◽  
The Difference

A matching algorithm that evaluates the difference between model and calculated flows for obstacle detection in video sequences is presented. A stabilization method for obstacle detection by median filtering to overcome instability in the computation of optical flow is also presented. Since optical flow is a scene-independent measurement, the proposed algorithm can be applied to various situations, whereas most of existing color- and texture-based algorithms depend on specific scenes, such as roadway and indoor scenes. An experiment is conducted with three real image sequences, in which a static box or a moving toy car appears, to evaluate the performance in terms of accuracy under varying thresholds using a receiver operating characteristic (ROC) curve. For the three image sequences, the ROC curves show, in the best case, that the false positive fraction and the true positive fraction is 19.0% and 79.6%, 11.4% and 84.5%, 19.0% and 85.4%, respectively. The processing time per frame is 19.38msec. on 2.0GHz Pentium 4, which is less than the video-frame rate.

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