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Rapid multi-scale estimation target tracking method related to re-detection

A re-detection and target tracking technology, applied in the field of image processing and computer vision, can solve the problems of slow tracking rate and errors, and achieve the effect of improving tracking performance and accuracy.

Active Publication Date: 2019-08-27
NANJING UNIV OF INFORMATION SCI & TECH
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AI Technical Summary

Problems solved by technology

Although the LCT algorithm improves the robustness in the case of target occlusion to a certain extent, there is still a problem. It only judges whether re-detection is required by whether the maximum response value is lower than the threshold value. This method of discrimination will have a large error.
In addition, because it is necessary to build a pyramid model on the target to estimate the optimal scale of the target, this will make the overall tracking rate slower

Method used

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  • Rapid multi-scale estimation target tracking method related to re-detection
  • Rapid multi-scale estimation target tracking method related to re-detection
  • Rapid multi-scale estimation target tracking method related to re-detection

Examples

Experimental program
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Embodiment 1

[0069] The framework diagram of the system flow for re-detection target tracking is as follows: figure 1 As shown, it specifically includes the following parts: training filter template, positioning, detection, re-detection and model update.

[0070] (1) Step 1: Training filtering template. First, initialize the target tracker, mark the initial area of ​​the target, use the VGG-19 network to extract the deep features of the target, and establish the initial target template and scale template for the calculation of the target response value in the second frame.

[0071] The establishment of the target template is mainly divided into the following parts:

[0072] First, the classifier performs cyclic shift sampling centered on the target position on the image block with a size of M×N, and the generated sample set is denoted as x i , where i ∈ {0,...M-1}×{0,...N-1}. per sample x i Each has a corresponding regression label y i ,y i obtained from the Gaussian function. The p...

example

[0119] The present invention measures the performance of the tracking algorithm by the OPE (one pass evaluation) evaluation standard, selects 60 challenging video sequences from the OTB100 data set to analyze, and compares them with other trackers (DeepKCF, SAMF, KCF, CSK, DFT , CT, CACF and other 7 trackers) were compared under different challenge factors (illumination change, target deformation, motion blur, fast motion, in-plane rotation, out-of-plane rotation, target out of view, background clutter, low resolution, etc.) .

[0120] Figure 4 The sampling frames for the tracking results of the tracking method (DRKCF) ​​of the present invention and other seven trackers, from Figure 4 It can be seen from the figure that the tracker proposed by the present invention can track the target better than other trackers, even if the target is lost, it can find the target and continue tracking.

[0121] Figure 5 is the comparison between the tracking method (DRKCF) ​​of the prese...

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Abstract

The invention provides a rapid multi-scale estimation target tracking algorithm related to depth characteristics and re-detection. The characteristics of the target are represented through a deep learning method, and the characteristic expression capability of the target is improved. In the tracking stage, when characteristics of image blocks with different scales are extracted, through PCA dimension reduction, the calculated amount can be reduced, and the overall calculation speed is increased. On the basis of two discrimination indexes, namely a peak sidelobe ratio (PSR) and a confidence coefficient smooth constraint (SCCM), a new detection index is provided, so that the tracking reliability of the current frame can be more accurately measured. If the reliability of the current frame isrelatively low, a series of target candidate boxes are generated through an Edgeboxes method so as to carry out re-detection.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, learns the characteristics of the target through the method of deep learning, and realizes the precise tracking of the target through the method of re-detection when the target drifts. It can be applied to areas such as unmanned driving and video surveillance. Background technique [0002] Object tracking is a key problem in computer vision and has a wide range of applications in various fields such as video surveillance, behavior recognition, unmanned driving, and medical images. The purpose of target tracking is to estimate the target position for each subsequent frame given the initial position of the target in the first frame. At present, the main computer vision tracking methods mainly include tracking methods based on correlation filtering and tracking methods based on deep learning. [0003] Target tracking algorithms based on correlation filtering have developed rapid...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/2411G06F18/214
Inventor 胡昭华黄嘉净
Owner NANJING UNIV OF INFORMATION SCI & TECH
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