An Experimental Comparison of Block Matching Techniques for Detection of Moving Objects
The detection of moving objects in complex scenes is the basis of many applications in surveillance, event detection, and tracking. Complex scenes are difficult to analyze due to camera noise and lighting conditions. Currently, moving objects are detected primarily using background subtraction algorithms, with block matching techniques as an alternative. In this paper, we complement our earlier work on the comparison of background subtraction methods by performing a similar study of block matching techniques. Block matching techniques first divide a frame of a video into blocks and then determine where each block has moved from in the preceding frame. These techniques are composed of three main components: block determination, which specifies the blocks; search methods, which specify where to look for a match; and, the matching criteria, which determine when a good match has been found. In our study, we compare various options for each component using publicly available video sequences of a traffic intersection taken under different traffic and weather conditions. Our results indicate that a simple block determination approach is significantly faster with minimum performance reduction, the three step search method detects more moving objects, and the mean-squared-difference matching criteria provides the best performance overall.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 899415
- Report Number(s):
- UCRL-CONF-221486
- Country of Publication:
- United States
- Language:
- English
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