Persons

While solving the tracking of objects for infrared range images the obtaining of the necessary volume relevant basis for neuron network training is problematic enough. The main obstacles are the absence of the far infrared range images base and the complexity of calibration of cameras of various manufacturers for composing the base. Due to these causes and the complexity of marking the borders of the given range objects using the neuron networks, at the moment, is not expedient. In the paper the principles of object tracking on the infrared range images, based on obtaining the key points using the SIFT, SURF, ORB algorithms, have been considered. The specific features of the given methods have been distinguished, and also, the specifics of far infrared range images and of the objects observed on it, has been emphasized. The analysis of the approaches to preserve the features of tracking an object in transition to a new frame of a video image has been performed, their main advantages and disadvantages have been revealed. Based on the obtained results the modernization of the tracking algorithm using the Jarvis algorithm modification has been proposed. The estimation of the influence on speed of additional processing in the tracking algorithm has been experimentally performed. The proposed approach to tracking an object on the infrared image, composed on the ORB algorithm key points and the application of the modified Jervis algorithm, is invariant to turns and scaling of the sought for object and allows to watch it in conditions of high nonhomogeneity of the background and presence of similar objects. The application of the developed algorithm permits to avoid a considerable loss of speed with a significant increase of the object tracking efficiency.

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An approach to division into clusters of the objects on the IR-range images has been proposed. As the data for clustering a set of the key points-correspondences, obtained using the SIFT, SURF, ORB algorithms has been used. A typical object detection circuit has been compiled and the analysis of clustering algorithms has been performed. The constraints while using the existing approaches, which had pushed to creation of the clustering problem solution, have been revealed. The developed algorithm has been tested and has demonstrated its efficiency for the IR-range images. The results of the given approach to clustering can be used for classification of objects by area.

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