## 4 Experiment  
### Experiment Configuration  
**Baseline tracker. ** Two baseline trackers are selected according to several conditions to test our method. The first condition is that our method aims to overcome the problems occurring in the real-time environment; thus, we apply the proposed method to the online MOT framework. The second is to select validated trackers that are listed in the MOT Challenge [22]. The last condition is that open-source project models are selected to avoid polarization and to evaluate the performance fairly. In accordance with those conditions, we adopt MOTDT [28] and DeepSORT [27] as baseline trackers. MOTDT proposes a Faster RCNN-based hitmap generation model to filter the correct candidates from the expanded candidates by combining the current detection results with the previous tracking results. This approach shows effectiveness in situations where the data is noisy because the detector additionally finds objects that failed detection. DeepSORT uses the Kalman filter-based motion model proposed by SORT [26] and proposes a CNN-based appearance model. DeepSORT has a high dependency on detection results when constructing candidates, but it is effective in dynamic situations due to the simple association method. For the human detector to provide detection results for the two baseline trackers, we use YOLOv2 [23] 544 \(\times\) 544 trained with the VOC (2007 + 2012) [41] dataset. **Restoration module. ** We use the SRGAN (super-resolution GAN) [36] as the image restoration module used in the re-detection and site restoration sections. SRGAN applies an adversarial loss to solve the super resolution problem and proves that the pixel values to be interpolated can be located in a realistic manifold. It is more effective than existing models using MSE (mean squared error). Based on these properties, SRGAN is used to estimate high-resolution sharp images from low-resolution blurry images. The traditional SRGAN uses the high-resolution image and the reduced low-resolution image for the ground truths and the input data, respectively, to learn to estimate the high-resolution image from the low-resolution image. However, in order for the tracker to adapt properly to dynamic movements, it is necessary not only to estimate high-resolution images from low-resolution images but also to estimate sharp images from blurry images. Therefore, our image restoration module is trained using high-resolution sharp images for the ground truths and low-resolution blurry images for the input data. Figure 5 shows how to construct a dataset to train our image restoration module. To create one training data pair consisting of a low-resolution blurry image and a high-resolution sharp image, one chunk needs to be configured. This chunk is constructed by selecting an odd number of images from a set of images arranged in a chronological order. From the constructed chunk, the low-resolution blurry image can be obtained by averaging the images and scaling it down to a quarter, and high-resolution sharp images can be obtained from the middle image of the chunk. In order for our image restoration module to focus on restoring a person's image, we need to exclude the background of the learning image. Therefore, we detect and crop humans in high-resolution sharp images and crop the same positions in low-resolution blurry images to produce training data pairs. There are a few conditions in taking a video to construct a training dataset:  
1. It should aim for people who are not included in the benchmark set for fair evaluation. 2. To simulate a natural and precise blurry image, the image must be taken at a high refresh rate with dynamic movement. According to the above conditions, images are taken at a 240 Hz refresh rate, the chunk size is set to 7, and datasets of 31,640 pairs (body 21,220 pairs, face 10,420 pairs) are extracted from the images. The configuration for learning is based on the SRGAN default setting with 400 epochs. Figure 6 shows the examples of the restoration using our image restoration module.