自动化学报2018，Vol.44Issue(1)：25-34,10.DOI:10.16383/j.aas.2018.c160573
实验小鼠运动参数的模板匹配及粒子滤波提取方法
An Extraction Algorithm for Motion Parameters of A Laboratory Mouse by Model Matching and Particle Filtering
摘要
Abstract
Laboratory mouse is a kind of deformable object. Existing methods can hardly extract motion trajectories and posture details simultaneously from those continuous recorded videos. An object tracking method based on model matching and particle filtering is adopted to solve this problem. A geometry based part model and its motion state function involving moving velocity are proposed. A model-observation difference function is established as the observation model by comparing the foreground pixels in the binary image and the geometry part model. A basic particle filter is built with this observation function and the motion state function with multi-stochastic variables which follow an independent distribution. Comparison is made between the proposed method and the classical frame-differencing method,which proves that the novel part model is analogous with a physical mouse in shape and supports real-time extracting rate and high computing efficiency. The novel method is able to estimate precisely both motion trajectories and posture states, and avoid effectively the faults of head-tail confusion and reflection disturbance. Therefore the novel method provides a trust worthy means for later behavioral analysis for biologists.关键词
目标跟踪/粒子滤波/部件模型/实验小鼠/体态Key words
Object tracking/particle filter/part model/laboratory mouse/posture引用本文复制引用
张继文,梁桐,张淑平..实验小鼠运动参数的模板匹配及粒子滤波提取方法[J].自动化学报,2018,44(1):25-34,10.基金项目
国家自然科学基金(61403225),摩擦学国家重点实验室(SKLT09A03)资助Supported by National Natural Science Foundation of China(61403225),Project of State Key Laboratory of Tribology(SKLT09A03) （61403225）
