## 3 Method
### Re-Detection  
Since the online MOT framework does not consider the future state, the best choice is needed at every frame. Since the candidate for the association is generally proposed from the detection result, it has a high dependency on the detection result. Therefore, if the false detection on an object from an image with noise can be reduced, the tracking on the wrong object and the loss of the tracked object due to the detection failure can be prevented. Figure 2 illustrates our re-detection method. It aims to increase the detection reliability by re-detection after reconstructing the image for the ambiguous detection results that are not too low or not high enough. Firstly, the raw detection result is required to classify the ambiguous detection result. The raw detection result \(D^{origin}\) of the current input image \(x\) can be obtained using the pre-trained HumanDetector as follows. \[D^{origin}=\{d_{1},d_{2},\dots,d_{detectsNum}\}\gets HumanDetector(x) \tag{1}\]  
The variable \(d\) means a detected instance which includes the location information (\(x\), \(y\), \(w\), \(h\)) and the confidence \(c^{body}\), thus, \(d=(b^{detect},c^{body})\). Here, \(b^{detect}\) means the \(x\),\(y\) coordinates, area, and height values that make up the bounding box, and \(c^{body}\) means the reliability of the detected object. To classify an ambiguous detection set, \(D^{origin}\) should be separated into an ambiguous detection set \(D^{amb}\) and a verified detection set \(D^{verified}\) depending on whether re-detection is needed or not, respectively. We define the confidence threshold, \(\tau^{detect}\) and \(\tau^{amb}\) to classify detection results with the confidence that is not too low or not too high enough. \(\tau^{detect}\) stands for the most basic threshold for the detection, and \(\tau^{amb}\) is a threshold to find ambiguous detections by ignoring low confidences. This process is defined as ambiguous detect filtering and is formulated as:  
\[D^{verified}=\left\{d\mid c^{body}\geq\tau^{detect},d\in D^{origin}\right\} \tag{2}\]  
\[D^{amb}=\left\{d\mid\tau^{detect}>c^{body}\geq\tau^{amb},d\in D^{origin}\right\} \tag{3}\]  
Figure 2: Schematic representation of the proposed re-detection method. It illustrates how the re-detection method works according to the data flow. If the value of the confidence \(c^{body}\) is lower than \(\tau^{amb}\), the target is determined to be a non-object, thus it is excluded from the detection set and not used in the tracker. For the classified ambiguous detection set \(D^{amb}\), the re-detection set \(D^{releted}\) can be obtained using the following definition. It functions to restore the ambiguous detection regions and then re-detect them by reusing existing detectors. The RestorationModule used to restore damaged images uses a GAN-based image inference model. The detailed procedure of training the module to optimize our model is described in Section 4.1. \[D^{releted}\gets HumanDetector(RestorationModule(Crop(x_{current},D^{amb}))) \tag{4}\]  
To classify reliable detections from the re-detection result set \(D^{releted}\), we use the following definition. It classifies the re-verified detection using the detection threshold \(\tau^{releted}\) for the confidence \(c^{releted}\). \[D^{re-verified}=\left\{d\mid c^{releted}\geq\tau^{releted},d\in D^{releted}\right\} \tag{5}\]  
As a result of these processes, a combination of \(D^{verified}\) and \(D^{re-verified}\) can be used to construct a detection set \(D^{complete}\) to be used for the tracking association. However, since the re-detected object may indicate the same object as the existing detection result, there is a possibility that the duplicate objects exist in \(D^{complete}\). Therefore, NMS (non-maximum suppression) is performed to remove the redundancy after constructing the union. The following definition refers to the NMS process, where \(\tau^{atoms}\) is the IOU (intersection over union) threshold for the NMS.