Abstract
Abstract. In this paper, a new technique for real-time object detection and tracking is presented. This technique is based on the geometrized histograms method (GHM) for segmenting and describing color images (frames of video sequences) and on the facilities for global image analysis provided by this method. Basic elements of the technique that make it possible to solve image understanding problems almost without using the pixel arrays of images are introduced and discussed.A real-time parallel software implementation of the developed technique is briefly discussed. This technique is applied to solving problems of road scene analysis. The application to finding small contrast objects in images, like traffic signals and signal zones of vehicles is given. The developed technique is applied also to detecting other vehicles in the frame. The results of processing different frame of videos of road scenes are presented and discussed.
Highlights
Considerable progress in object detection and recognition has been achieved during the last decade
In all systems based on learning, there are some problems in automatic testing the validity of the obtained results and in revealing the interaction of the found objects
We propose several applications of the geometrized histograms method (GHM) (Kiy, 2018), (Kiy, Anokhin, Podoprosvetov, 2020), in object detection, recognition, using the ability of the method to perform the global image analysis
Summary
Considerable progress in object detection and recognition has been achieved during the last decade. In all systems based on learning, there are some problems in automatic testing the validity of the obtained results and in revealing the interaction of the found objects (global image analysis). This means that the development of methods based on the classical pattern recognition approach is of interest. We propose several applications of the GHM (Kiy, 2018), (Kiy, Anokhin, Podoprosvetov, 2020), in object detection, recognition, using the ability of the method to perform the global image analysis
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