Chunk-aware hash tracking method with shadow removal

Abstract

The invention discloses a blocking perception Hash tracking method with shadow removing. The method comprises the steps of: determining shadow areas in an image according to the distribution characteristics of a shadow image in each channel grey-scale map of a CIELAB color space; then utilizing a color constancy theory to recover pixel points in the shadow areas to a non-shadow effect; combining blocking perception Hash coding values with color self-similarity for forming a similarity measure, and carrying out matching on tracked target sub-blocks of adjacent frames based on the similarity measure; and finally, combining the above sub-blocks to obtain the regional position of the tracked target in the current frame, and realizing the tracking of the tracked target in a video. The blocking perception Hash tracking method has the advantages that according to different moving ranges and deforming degrees of human body parts, a human body target is divided into eight sub-blocks, and on this basis, and the blocking perception Hash coding method is provided to solve the problem of an existing tracking algorithm that the tracking is unsuccessful when the human body is partially or totally shielded or partially rotated and when the illumination in the shadow areas and non-shadow areas of a natural scene changes suddenly.

Description

technical field [0001] The invention relates to video image tracking technology, in particular to a block-aware hash tracking method with shadow removal. Background technique [0002] With the rapid development of computer technology, the need to use computers to realize various intelligent functions of human beings has gradually become a reality. Among them, using computers to simulate human vision to obtain cognitive external environment information has achieved rapid development in decades, and has therefore become a popular research topic in the field of computer science. Among them, video tracking technology is one of the important research contents of computer vision, and it is also a difficult problem that has not been fundamentally solved in the current computer vision research. [0003] The existing tracking methods mainly include target area-based tracking, target feature-based tracking, optical flow field-based target tracking, target model-based tracking and pre...

AI Technical Summary

Problems solved by technology

However, these methods have their own technical shortcomings: 1. The tracking method based on target area matching needs to track target matching for all areas, which takes a long time to search, and the tracking effect is not good when the tracking target is partially occluded; 2. Based on The feature tracking method is based on one or some features of the tracking target to match the tracking target in adjacent frames. It is usually difficult to select an appropriate feature to represent the tracking target, and it is difficult to balance the number of features and the efficiency of the algorithm; 3. Based on the optical flow field The tracking method is to obtain the flow velocity at the feature point through feature matching, but it is difficult to extract the precise shape of the moving object due to the sparse optical flow field; 4. The tracking method based on the target model usually uses a line graph model, 2D The model and 3D model represent the tracking target. This method can deal with occlusion and obtain more data required for behavior analysis, but the disadvantage is that it is very difficult to obtain accurate geometric models of all moving targets in the monitoring scene, and the amount of calculation is huge. It is difficult to achieve real-time performance; 5. Tracking methods based on prediction mainly include Kalman filter method, particle filter, etc.

The Kalman filter can effectively perform linear optimal estimation, but this method cannot handle nonlinear and non-Gaussian problems

The particle filter method can be applied to nonlinear and non-Gaussian motion systems, but the disadvantage is that it has a large amount of calculation and poor real-time performance.

Images