# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 2 Background
### Tracking Algorithm  
The KLT tracker was introduced by [PERSON] and [PERSON] and extended by [PERSON] and [PERSON]. The KLT tracker tracks an object in two simple steps. First, it finds the traceable feature points in the first frame, and then uses the calculated displacement to track the features detected in the next frames. The feature points of an object are tracked using an optical flow tracker [18]. The channel and spatial reliability (CSRT) tracker was proposed by [PERSON] et al. [19], and improves the discriminative correlation filter (DCF) tracker by introducing spatial and channel reliability. The spatial reliability map was used to resize the filters to find optimal filter sizes by taking advantage of non-rectangular targets. The KCF tracker proposed by [PERSON] et al. [20] is a tracker that extends the MOSSE tracker. The KCF tracker improves the tracking accuracy by extracting the histogram of gradient (HoG) function, but requires more data. Therefore, the data are calculated in the Fourier domain to speed up the data processing procedure. The MedianFlow tracker proposed by [PERSON] et al. [21] tracks the point of each object forward and backward, and compares the results of the trajectory. If the object is tracked correctly, the trajectory forward and backward will yield the same results; otherwise, a tracking error will indicate the opposite value. The error was used to track points inside the bounding box and classify the inliers and outliers. The MIL tracker proposed by [PERSON] et al. [22] conducted object tracking similar to the online boosting approach. The MIL used a set of image patches (called bags) instead of a single sample for training. For classification, the MIL uses a multi-instance learning approach that considers object bags by grouping similar samples into such bags. A bag containing more than one positive example was called a positive bag, which is otherwise called a negative bag. This technique not only prevents the MIL tracker from losing important information, it also prevents mislabeling problems. The MOSSE tracker proposed by [PERSON] et al. [23] achieved high efficiency by calculating the correlation in the time domain. The correlation filter improved the efficiency for scaling, rotation, deformation, and occlusion problems from the traditional approach. The MOSSE is more efficient than the other correlation filter-based trackers because the target does not need to be at the center of the image initially. The TLD tracker proposed by [PERSON] et al. [24] was a technique for long-term tracking and consisted of three parts: tracker, learner, and detector. The tracker aimed to track the objects in successive frames, and the learner used the P-expert and N-expert to estimate false positives and false alarms to update the detector. In addition, the detector finds targets based on the appearance model, provides an output to the learner, and modifies the tracker if necessary. The goal of the TLD is to improve the robustness of the tracking by disabling the online learning when an object is out of the frame or completely obscured by another object, preventing learning through misinformation. In addition, the detection component allows the TLD to redetect the object if it later appears in the video again.