Self-adaptation target tracking method based on vision saliency characteristics

Abstract

The invention discloses a self-adaptation target tracking method based on vision saliency characteristics. The method is characterized by comprising the following steps of (1) building a color image quaternion model, extracting and processing characteristic information of four channels of the image, and detecting an image vision saliency map; (2) on the basis of the detected vision saliency map, extracting a vision saliency characteristic kernel histogram, and extracting the similarities among Bhattacharyya coefficient matric characteristics; (3) determining a characteristic fusion strategy, and calculating self-adaptation fusion weights of image color characteristics and vision saliency characteristics; (4) on the basis of Bhattacharyya coefficients, determining target center position transfer vector weights, and carrying out iterative optimization to obtain the final target center position. According to the self-adaptation target tracking method, background fusion interference can be effectively overcome, and robustness is achieved for target portion shielding.

Description

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AI Technical Summary

Benefits of technology

This technology solves problems with current methods for detecting targets by combining data from different sources like cameras or radar sensors. These techniques help identify specific objects that may have been hidden behind other things during an event such as accidental damage caused due to firefighting equipment being used on fires at nearby locations. Additionally, this method allows accurate detection even if there are small variations between similar areas where no object was detected beforehand.

Problems solved by technology

Technological Problem addressed by this patented technology relates to improving object tracking speed without being affected by environmental factors such as light or shade variations during image capture due to camera movement. Current methods have limitations including poor visibility caused by clothing, lack of focus ability from surrounding areas, noisy environment, etc., making them difficult to achieve accurate tracking over dynamic environments like videos captured through cameras.

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