Classification and Regression Models to Evaluate Human Motion Patterns from Image Sequences

• Main Authors: Cho, Chien-Wen, 卓建文
• Other Authors: Chen, You-Yin
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/22243854499934904430

博士 === 國立交通大學 === 電機與控制工程系所 === 98 === Human motion analysis has been treated as an investigative and diagnostic assistant tool in medicine, sports, and surveillance. Generally speaking, there are two categories of approaches for human motion analysis: sensor-based and vision-based. The drawbacks of sensor-based methods are possible discontinuous records, affection of motion patterns, incomplete observation of the motion of the body, and uncomfortableness. Early vision-based methods like motion capturing overcome above disadvantages but still are expensive, time-consuming for preparation, limited to specific lab. Recent vision-based methods improved above demerits but might be critical to segmentation and parameter estimation, use inefficient features, and thus be low accuracy. In addition, regressions in human motion analysis is essential but there are very limited studies on it. Therefore, we are intended to develop new evaluation systems for the analysis of human movement patterns for both classification and regression. We use a model-free strategy and thus avoid the critical demands of segmentation and parameter estimation. Furthermore, we use linear discriminant analysis to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression is also achieved by assessing spatial and temporal information through classification and finally by using these two new indices for linear regression. Our systems are designed to be capable of classifying human gait patterns, predicting the abnormality degree a given gait image sequences, recognizing human activities, and detecting the drowsiness of people. For judging the identification of normal people, the accuracy was 93.63%. For separating PD gaits from normal people, it was as good as 95.49%. According to the experiments, the outcome had correlation to the sum of the UPDRS Part III sub-scores with r = 0.9109. For human activity recognition, we improved the disadvantages of traditional approaches by using both shape-based features by LDA classification and temporal correlations by fuzzy inference. The approach was tested to be robust and had a very good recognition accuracy of 91.78%, which was higher than nearest neighbor and HMM. For drowsiness detection, we designed a non-intrusive method to avoid the inconvenience caused by traditional approaches. On the other hand, we also improve the detection accuracy of drowsiness by a fuzzy integral based fusion method combining PERCLOS and LDBF of the eyes. The approach had very high drowsiness detection accuracy of 95.1%. Our method was also implemented to be a PC-based real-time system. The proposed algorithm was implemented in a real-world driver drowsiness detection and warning system. In summary, we developed several models to address four important issues in human motion analysis: the classification and regression of human gaits, the classification of human activity, and the detection of human drowsiness. In this dissertation, we take the vision-based approaches by using only a camera. Thus, our methods are all non-intrusive, easy to implemented, cost-effective, comfortable for subjects. We also verified our models by comparing the performance with traditional methods. As a result, our method was an objective and cost-efficient assistant way to provide accurate human motion analysis. Keywords: human motion analysis, gait, activity, drowsiness, vision-based, linear discriminant analysis, classification, regression.