Research On Target Detection And Tracking Algorithms Based On Monocular Vision

Target detection and tracking algorithms are studied systemically in this paper.The project background of this paper is the vehicle detection system based on the monocular vision.Special emphases are placed on model-based target detection and tracking,object tracking based on mean shift and object segmentation and tracking based on active contours.The paper consists of six chapters.Firstly,a survey of the target detection and tracking algorithms is made in the first chapter.The merits and defects of target detection and tracking algorithms are analyzed generally and the status quo of the research, structure of the vehicle detection system and research background is introduced.Model-based target detection and tracking algorithms are studied in the second chapter.A new model matching algorithm based on curve projection and robust tracking algorithm based on model matching and region correlation fusion are presented.The new model matching algorithm allows partial matching between models and object feature and improve adaptive capacity of target detection and tracking algorithm to occlusion.The new algorithm can adapt illumination changes since it use edge as target feature.The algorithm integrates shape feature and appearance feature after fusion of region correlation and model matching and increase the ability of resistance to background interference and made the algorithm has strong robustness. The idea of partial matching is introduced to the appearance based tracking algorithm and a new tracking algorithm based on fusion of SSD and MCD is presented.The number of pixels that its distance from template pixel is less than threshold and SSD of marching area are both taken into account in the new method.Experimental results show that the new algorithm can provide stable tracking for a long time and be better than the SSD-based and MCD-based method obviously,especially in the case of occlusion.The studies of tracking algorithm based on mean shift is placed in the chapter three.An improved algorithm based on block color histogram and mean shift is presented based on the studies of original algorithm.The new algorithm uses block color histogram as the target representation and improves robustness since targets distinguish ability is increased.A target scale and rotation matrix is imported into the algorithm and makes the algorithm have the ability to adapt to target rotation and scaling.A discuss of method using mean shift in the other tracking algorithm is present in the end of chapter three.A new active contour model named worm based on bionics is present after studies of some familiar active contour model in the chapter four.The new algorithm allows automatic topological changes and object exterior contours and interior contours are automatically detected simultaneously.The model use 2D regions as representation of active contours avoids the problem of high computational complexity brought by the level set converting 2D problem to the 3D.Comparing with the C-V model,the worm model allows the simultaneous detection of several objects with different intensity.The new model can segments object with weak edge and has the ability of automatically detection of interior contours comparing to the geodesic active contours. The worm model integrates merits of region-based and edge-based active contours and has strong adaptive capacity and flexibility.The vehicle detection and tracking system developed by the author is introduced in detail in the chapter five.The parts of the system are introduced one by one,including region of interesting detection based on shadow detection,model based vehicle detection and tracking,occlusion situation management,vehicle distance measurement.The system flow chart and experiment result is also presented in the chapter five.The conclusion and suggestion for future research is presented in the last chapter.