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Abstract. This paper presents a 3D feature-based people tracking algorithm which combines an interacting multiple model (IMM) algorithm with a cascade multiple feature data association algorithm. The IMM algorithm in this paper only uses an adaptive Kalman Filter and two dynamic models consisting of a constant velocity model (CV) and a current statistics model (CS) to predict the 3D location of people maneuvering and update the prediction with corresponding measurement. The cascade multiple feature data association algorithm in this paper utilizes three hypotheses, including the nearest distance hypothesis, the velocity consistency hypothesis, and the intensity consistency hypothesis, in turn to determine which trajectory a measurement should be assigned to. Experimental results demonstrate the robustness and efficiency of the proposed framework. It is real-time and not sensitive to the variable frame to frame interval time. It also can deal with the occlusion of people and do well in those cases that people rotate and wriggle.

SUMMARYThere has been much research in recent decades on character recognition methods, and some methods have already been put into practical use. There are many unresolved problems, however, with respect to handwritten character recognition as composed with printed character recognition. The authors considered discriminant functions, which constitute the most important part of a character recognition method. As a result of considering problems of conventional statistical discriminant functions, the authors propose applying the fuzzy theory to discriminant functions. The so-called fuzzy discriminant function is capable of representing a data distribution in a more flexible manner because it consists of membership functions on the principal axes of learning samples.The authors conducted recognition experiments for handwritten characters with two types of membership functions. In one type the membership values are directly tuned based on human experiences; in the other they are derived from histograms or statistical data. With the former membership function, the recognition rate of 99.0 percent is achieved for 'numeric' characters from the handwritten alphanumeric data base ETL6, and with the latter, the rate of 96.0 percent for 'hiragana' characters from handwritten educational 'kanji' data base ETL8. This result proves the effectivenessof the fuzzy discriminant function. It also indicates that a dynamic combination of 36 human experiences and statistical techniques is a key to practical systems.