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Pedestrian Tracking Method under Occlusion and Scale Variation

A pedestrian tracking and scale change technology, applied in the field of computer vision, which can solve problems such as tracking failure

Active Publication Date: 2020-05-01
BINHAI IND RES INST OF TIANJIN UNIV CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The generative method only considers the target information in the tracking process and ignores the background information, which can easily lead to tracking failure in the case of background interference

Method used

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  • Pedestrian Tracking Method under Occlusion and Scale Variation
  • Pedestrian Tracking Method under Occlusion and Scale Variation
  • Pedestrian Tracking Method under Occlusion and Scale Variation

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Experimental program
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Embodiment Construction

[0029] A pedestrian tracking method in the case of occlusion and scale changes. The Kalman filter is used to predict the target position, and the prediction result is used in the KCF framework to accurately locate the target; according to the KCF tracking response value, it is judged whether the target is occluded. When occlusion occurs, the tracking result of KCF is invalid, the filter is not updated, and the prediction result of Kalman filtering is used for calibration; when no occlusion occurs, the filter is updated, and the tracking result of the filter is used for Kalman calibration to obtain the final target position.

[0030] The specific steps are as follows:

[0031] (1) select the video sequence to be tracked, including video sequence, groundfruth text file and frames text file;

[0032] (2) Initialize the correlation filter through the first frame information, and minimize the loss function to find α=(K+λI) -1 y, where I is the identity matrix, y represents the lab...

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Abstract

The invention discloses a pedestrian tracking algorithm under a situation of blocking and scale change. The algorithm comprises the following steps of: through Kalman filtering, predicting a target position; using a prediction result for a KCF (Kernel Correlation Filter) framework to accurately position a target, according to a KCF tracking response value, judging whether a target is blocked or not; if the target is blocked, proving that a KCF tracking result is ineffective, stopping updating a filter, and carrying out calibration by the prediction result of Kalman filtering; and when the target is not blocked, updating the filter, and meanwhile, using the tracking result of the filter to carry out Kalman calibration to obtain a final target position. By use of the algorithm, under the situation that the target is blocked or the target scale is changed, the target can be accurately tracked, data processing speed is about 82 frames / second, and an instantaneity requirement can be basically met.

Description

technical field [0001] The invention belongs to the field of computer vision, in particular to a pedestrian tracking method under the condition of occlusion and scale change. Background technique [0002] Object tracking is an important research topic in the field of computer vision, and it has broad application prospects in video surveillance, human-computer interaction, robotics, military guidance and other fields. Object tracking based on image sequences is to determine the location of the object and its trajectory according to the given object bounding box in the first frame without any prior knowledge. Although the field of target tracking has achieved rapid development in recent years, it still faces many challenges: background interference, motion blur, target deformation, illumination changes, fast movement, occlusion, low resolution, rotation and scale changes, etc., which will lead to Drift or even tracking failure occurs in the process of target tracking. Among ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/277
CPCG06T7/277G06T2207/10016G06T2207/20081
Inventor 刘梦娜陈胜勇刁琛程徐栾昊
Owner BINHAI IND RES INST OF TIANJIN UNIV CO LTD