Tracking and guiding multiple laser beams for beam

Authors: PengCheng Zhang De Xu

Publish Date: 2015/11/06

Volume: 12, Issue: 6, Pages: 600-610

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Abstract

An accurate and robust approach for tracking and guiding multiple laser beams is developed which can be applied to the task of beam and target alignment Multiple laser spots are firstly detected and recognized from the image sequences of the target and laser spots Then the contour tracking algorithm based on the chain code is investigated in which the shape matching scheme based on the invariant moments is employed to distinguish different spots When occlusion occurs in the multiple spots tracking procedure the contour tracking combined with Kalman filter prediction is proposed to obtain the positions of multiple spots in realtime In order to guide 3 spots to align the target an incremental proportional integral PI controller is employed to make the image features of spots converge to the desired ones Comparative experiments show that the proposed tracking method can successfully cope with the fast motion partial or complete occlusion The experiment results on spots guiding also exhibit the accurate and robust performance of the strategy The proposed visual system solves the problem of spots mixing reduces the alignment time improves the shooting accuracy and has been successfully applied to the experimental platformThe detection and segmentation of multiple spots in image sequences is a fundamental step before the tracking or when the tracking is failed The laser spot is assumed to be the only part of the image with a high light intensity The common approach is to extract the spots by background subtraction However in practice the intensity of laser may randomly change the illumination in the scene may not be very stable and even the intensity of a cylindrical target may vibrate slightly All of these may induce the changes of background Stauffer and Grimsor3 modeled each pixel as a mixture of Gaussians and used an online approximation to update the model However the method suffers from slow learning at the beginning and is not sensitive to the small motions An improved Gaussian mixture model was presented by Zivkovic4 in which not only the parameters but also the number of components of the mixture were constantly selected for each pixel However this method has high computational cost especially in the face of the high image resolutionThe tracking algorithm can provide the position feedback for the spot in realtime In 5 the object tracking was divided into point tracking kernel tracking and silhouette tracking Features such as color edges optical flow and texture are often chosen for tracking They can be represented by a probabilistic model and then detected in consecutive frames In general the most desirable features of spot are the ones which can be used to distinguish one spot from the others Considering the gray image the biggest difference between different spots is the contour then the intensity Moreover due to the fact that the spot is nonrigid the tracking algorithm based on its contour is more practical Many contour models have been reported for tracking in the previous literature such as optical flow level set snakes balloons and active contours model6 7 8 Some contour tracking methods based on boundary codes were investigated in 9 10 However they are limited to the boundarybased information and are sensitive to noise In order to overcome the drawbacks of the sensitiveness to noise and poor image contrast a particle filtering algorithm for geometric active contours tracking was proposed in 11 However these techniques require a number of iterations and are computationally too expensive for realtime application on multiple spots tracking It is necessary to develop one rapid and robust contour tracking scheme for spotsAnother important problem is that the spots may mix together and interfere with each other when multiple laser beams simultaneously shoot the target The problem may be solved by guiding a single beam at a time however which is timeconsuming Therefore occlusion handling is a difficult issue in the face of multiple spots tracking In the previous literature the appearance model was incorporated or the target was treated as a blob which may merge and split or an exclusion principle was employed by using the joint probabilistic data association filter and the particle filter was employed to avoid the high computational load12 13 14 15 The prior information of shape is often integrated into contour representation Yilmaz et al16 proposed a nonrigid tracking method which was achieved by evolving the contour from frame to frame with some energy functions The contour represented by level sets was used to recover the missing object regions during occlusion However these methods require precise model increase the computational complexity and may fail in the face of the rapid motionsThe recognition of multiple spots is also a key problem during multiple spots tracking In order to distinguish different spots shape representation and matching techniques should be considered A number of successful shape matching algorithms were proposed One of the most popular methods is to use Hausdorff distance although it is very sensitive to outliers Some methods compare shape by the feature vector which contains the descriptors such as area geometric moments shape matrix appearance via gray histograms optical flow vectors etc17 while others directly do with the aid of pixel brightness18 Belonggie et al19 proposed the shape context for shape matching and object recognition which described the contour points by histogram in the logpolar space The similarity between two shapes was computed by a sum of matching errors between corresponding points However a large amount of calculations will be needed and they are hard to satisfy the realtime requirement

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