Research And Applications On The Key Technology Of Target Detection And Tracking

Machine vision as a specific field of photoelectric technology has rapidly grown to bea promising industry. On the support of the National Science and Technology MajorProject, this paper studies the most important and widely used algorithms with itsapplications in the object detection and tracking field. The major research and innovation:For the case of a target with known features, the paper proposed an object detectionmethod based on the weighted color histogram. By the texture information of pixels, thecolor feature can be weighted to generate the two-dimensional color feature histogram.Then, the detection method uses it to search the desired object in the image space. Theexperimental results confirmed that this method can obtain better performance than theconventional algorithms. This paper also verified its effectiveness on a robot workpiecesorting system.For the application requirement of material classification, this paper proposed aclassification algorithm based on the weighted diffusion shape context which uses thecurvature weighted diffusion mechanism to compensate the effect of image distorted noise.Moreover, it uses the dynamic programming for feature point matching to reduce thecalculation time cost. The algorithm achieved good results in classification of workpieces.For the case of a target with unknown features, the paper proposed a backgroundmodeling method based on Weber Local Descriptor and the Kernel Density Estimation.This method makes kernel density estimation for each pixel using Weber Local Descriptoras its feature information, and then designs the sample update mechanism and adaptivevariance to enhance the algorithm robustness. The experiments have been carried oninvolving the accuracy and robustness of the algorithm. The infrared night vision and beltconveyor application were also tested.For the object tracking problem in video scene, the paper proposed a particle filtertracking algorithm based on adaptive sparse expression. This algorithm constructs thelocal sparse express modeling within the particle filter tracking framework, and uses theaccelerated gradient method to improve the time consumption. The online templatesupdate and sparse dictionary update strategy were proposed to improve the robustness ofthe algorithm. The system re-sampling mechanism is used to overcome the problem ofparticle degeneracy. Finally, the experimental results confirmed that the algorithm had better tracking accuracy and robustness, and could meet the tracking requirements underbelt conveyor environment.An industrial robot target tracking pickup system was designed by the object detectionand tracking algorithm in this paper based on a Huazhong CNC HNC-08system. Animplementation of this system on a MOTOMAN SK6industrial robot was built for pickupexperiments on belt conveyors.