Moving object detection using strip frame images

In existing methods of moving object detection using image recognition technology, an obtained whole image is processed and a moving object is detected in the picture. However, when the moving portion of the scene is hidden by obstacles, recognition of a moving object is sometimes difficult. In this study, we propose a novel method of discriminating moving objects, such as a person or a vehicle. We use only a narrow and tall area of the video, called the Strip Frame Image, for detecting moving portions. By using this method, we are able to obtain patterns of moving objects while avoiding obstacles in a picture. We then classify them by DP matching against previously stored reference patterns in a database for all possible classes (person, bicycle, car, bus). In this paper, we compare several variations of an algorithm used to detect and classify objects passing laterally in front of a security camera. We test both a moving object speed‐independent method and a speed‐based method for constructing patterns for the passing objects. The results show that the relatively simple method of pattern classification by DP matching can be successfully used for classifying graphic objects of a certain degree of complexity. Finally, nonhomogeneity of people's patterns and their subsequent frequent misclassification is addressed not by producing reference patterns for people, but by differentiating them from correctly classified bicycles by the DP distance to the first candidate. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(4): 38–47, 2010; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/eej.21095