Title :
Real-time pedestrian tracking by visual attention and human knowledge learning
Author :
Zeng, Linhua ; Sun, Yaoru
Author_Institution :
Dept. of Comput. Sci. & Technol., Tongji Univ., Shanghai, China
Abstract :
In this paper, a novel model of pedestrian tracking by using object-based attention and human knowledge is presented. The selective units in the system are the objects and groupings which are space-driven as well as feature-driven. The factors of speed, motion direction and spatial location are used to cluster and form the groupings. Hierarchical selectivity of attention for objects in a grouping is implemented under the guide of human model knowledge with the help of a head detector. The motion cues are utilized to tackle the multi-person tracking through hierarchical selection of attention. The experimental results from outdoor environments are reported.
Keywords :
computer vision; feature extraction; image motion analysis; learning (artificial intelligence); object detection; object tracking; pattern clustering; head detector; hierarchical selectivity; human knowledge learning; human model knowledge; motion cues; multiperson tracking; object clustering; object grouping; object motion direction; object spatial location; object speed; object-based attention; real-time pedestrian tracking; visual attention; hierarchical selectivity; human knowledge; motion cue; pedestrian tracking; visual attention;
Conference_Titel :
Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6788-4
DOI :
10.1109/PIC.2010.5687433
