2d human motion estimation modeling for classification
| dc.contributor.author | Chan; Choon Kit | |
| dc.date.accessioned | 2021-03-17T07:38:30Z | |
| dc.date.available | 2021-03-17T07:38:30Z | |
| dc.date.issued | 2016-04-01 | |
| dc.description.abstract | Human motion estimation is an approach to predict motion activities from static body postures; widely explored from gait motion, silhouette-based, biomechanical-based or image-based analyses for motion capture, recognition and vision surveillance purposes. Human motion is often captured via Marker-Based (MB) and Marker-Less (ML) system by using single or multiple cameras. These motions are commonly analyzed in 3-Dimensional (3D) or 2-Dimensional (2D) positioning involving location and orientations of body joints. Nevertheless, owing to the complexity of high dimensionality motion data, this study has focused on the 2D human motion. Existing developed 2D stick figures could hardly point the exact body joint location. Besides, no researchers have considered the tolerance adjustment for human motion estimation. Therefore, the main goal of this study is to develop a 2D stick estimation model with error tolerance to represent human motions for classification analysis. The 2D stick estimation model is developed from three fundamental body segments: Backbone (BB), Upper Body (UB) and Lower Body (LB). Considering the capability of body segments’ stretches while performing different activities, tolerance model is derived from the average deviations of polynomial fitting coefficients evaluated at sequential time steps. Integrating the precedent time-step coordinates with the tolerance model iteratively yield the estimated body joint coordinates at subsequent time step. The developed model is tested on (i) MB basic motions: walking, running, jumping and MB sports motions: punching, sword playing and taichi from CMU database and (ii) ML basic motions: walking, running, jumping from YouTube and ML sports motions: Yoga motion of child’s, leg lock and camel pose from experimental captures. Data transformation is initiated to snapshot the video data into still images followed by image transformations into coordinate data. Data elimination cum regression imputation is carried out to treat missing data found from occlusion and hidden body segments. The motion estimation model for tolerance consideration is performed on three 2D motion estimation techniques: IVE-SAT, AVE-SAT and AVE-TAT. The 2D stick estimation model is judged on matching analysis and classification accuracies using Lazy classifiers. Findings show that the developed 2D stick estimation model by AVE-TAT resulted in best matching accuracy up to 66.67% and classification accuracies above 90% for all motion categories. The developed model has the advantage over its ability to estimate human motions specifically with error tolerance adjustment resembling the body segment stretches throughout the entire activity. The study outcomes successfully imply that the proposed 2D stick estimation model with AVE-TAT is a feasible approach in distinguishing characteristics of different human motions for classifications. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12224 | |
| dc.language.iso | en | en_US |
| dc.title | 2d human motion estimation modeling for classification | en_US |
| dc.type | Thesis | en_US |
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