Title :
Tracking moving objects with co-evolutionary snakes
Author :
Liatsis, P. ; Ooi, C.
Author_Institution :
Dept. of Electr. Eng. & Electron., Univ. of Manchester Inst. of Sci. & Technol., UK
Abstract :
A new symbiotic genetic algorithm (SGA) based active contour model (snake) is proposed to track the B-spline contour of obstacles. It exploits the local control properties of the B-spline to decompose the contour into subcontours and optimizes each subcontour in separate genetic algorithms (GA). Unlike the GA-based snake, an SGA snake can track the obstacle´s outline more robustly. Application-specific inter-population genetic operators are introduced to reinforce the symbiotic relationship via migration of genetic material. The use of symbiosis dramatically reduces the combinatorics of the search space, when compared to GAs. Results of tracking objects in real road scenarios demonstrate its robustness to noise and stability of convergence when compared to its GA counterpart.
Keywords :
convergence of numerical methods; genetic algorithms; image motion analysis; object detection; splines (mathematics); target tracking; B-spline contour; active contour model; image object location; inter-population genetic operators; moving object tracking; obstacle outline; real road scenarios; snake; symbiotic genetic algorithm; Active contours; Combinatorial mathematics; Control system synthesis; Deformable models; Genetic algorithms; Noise robustness; Robust stability; Shape control; Spline; Symbiosis;
Conference_Titel :
Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom
Print_ISBN :
953-7044-01-7
DOI :
10.1109/VIPROM.2002.1026677