Feature extraction and pattern matching are the key problems in recognition and tracking system of multiple objects. In order to extract the features of stacked steel bars in real production environment, a method was proposed, which consisted of connected objects segmentation and multiple objects recognition. Median filter and morphological filter were applied in the steel bars image to remove the noise. Adaptive thresholding and watershed transform were used to segment the connected bar objects. Object centroid as the features was computed by means of regional statistics, parameter recognition, noise region removal and cluster analysis. For the image sequence of steel bars, the object tracking chain was established with template matching, near displacement matching and Kalman filtering. Target tracking was updated with inserting, deleting and refreshing of tracking chain nodes. The potential missing objects and false incremental ones were corrected in the counting result. At the production line 100 frames of sequential images were captured, and the tracking and counting method get the accuracy of 96.2%.
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