Method and system for automatically tracking moving pedestrian video based on particle filtering

Abstract

The invention discloses a method and a system for automatically tracking a moving pedestrian video based on particle filtering. The method comprises the following steps of: inputting one frame of images, and carrying out detection through an HOG (Histogram of Oriented Gradient) feature vector set and an SVM (Support Vector Machine) vector machine; in order to realize particle filtering tracking based on double HOG and color features, firstly obtaining an initial rectangular area of target pedestrian, sampling a plurality of particles from a target rectangular area, extracting an HOG feature and a color feature, computing the weight of the particles after the double HOG and color features are fused, obtaining the final state estimation through a minimum mean square error estimator, outputting an estimation target and then resampling; and closely locking the tracked target pedestrian. The method extracts the double HOG and color features to increase the robustness of a particle filtering likelihood model and eliminate the unstable situation in the tracking process, the method combines the HOG feature to build the better likelihood model through a fusion strategy of weighted mean, the robustness of the tracking algorithm is greatly increased, and the stable tracking is completed.

Description

technical field [0001] The present application relates to the technical field of computer vision, in particular to a method and system for automatic video tracking of moving pedestrians based on particle filtering. Background technique [0002] The tracking of moving objects is the corresponding matching problem of the position, size, shape and other related features of the moving pedestrians on the continuous image sequence. Many scholars have proposed many algorithms. There are mainly four kinds of existing moving object tracking algorithms: tracking method based on template matching, tracking method based on contour, tracking method based on motion prediction and particle filter tracking method. [0003] Among them, the particle filter tracking method is used in real application scenarios, usually the noise of the image does not obey the Gaussian distribution, therefore, Kalman filtering cannot obtain better tracking effect, in order to be applied to real application sce...

AI Technical Summary

Problems solved by technology

The disadvantage of the particle filter is that too many sampling points are required to ensure the correctness of the maximum likelihood estimation of the current state, resulting in an excessive amount of calculation.

[0006] Another shortcoming of the particle filter is the phenomenon of particle degradation. At present, the main entry point for many scholars to improve the particle filter is to start with solving the phenomenon of particle degradation.

Despite these improvements, there are no related reports on pedestrian tracking, and there are still many technical difficulties to be overcome

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