# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 1 Introduction  
Image-based reconstruction and modeling of objects [1; 2; 3], scene [4; 5; 6; 7], or processes [8; 9; 10; 11] is a widely accessible technique in terms of cost for information acquisition [12; 13; 14]. Structure from motion (SfM) is a photogrammetric technique for estimating the 3D structure of the object from a set of 2D images [15; 16; 17; 18]. It is the most popular technique for 3D object reconstruction. When the camera is positioned relatively close to the object being imaged (object size and the camera-to-object distance are both less than 100 m [19]), the technique is called close-range photogrammetry. Smartphone-based close-range photogrammetry is easily accessible to ordinary users for modeling objects at home [20; 21]. There exist a number of photogrammetry software as free/open-source and commercial packages: free/open-source applications for SfM [22]: Meshroom [23], COLMAP [24; 25], VisualSFM [26], OpenMVG [27], Regard3D [28], OpenDroneMap (ODM) [29], MultiViewEnvironment (MVE) [30], MicMac [31]; commercial solutions [32]: Pixpro [33], Agisoft Metashape [34], 3 Dflow 3 DF Zephyr [35], Bentley ContextCapture [36], Autodesk ReCap [37], CapturingReality Reality-Capture [38], PhotoModeler [39], PIX4 Dmapper [40], DroneDeploy [41], Trimble Inpho [42], OpenDroneMap WebODM [43], and Elcovision 10 [44]. One of the applications of close-range photogrammetry is the 3D modeling of the human head. Several common applications of head 3D reconstruction for home users could be getting head measurements in order to choose the appropriate size of headwear products ortrying out head appear. In general, 3D head modeling can be used in medicine [12; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57], psychological research [58; 59; 60], forensics [61], anthropology [62; 63; 64; 65; 66; 67], industry [68; 69; 70; 71; 72; 73; 74; 75], art and entertainment, virtual/augmented reality, fashion, education, biometrics, marketing, and social media. Image-based reconstruction and modeling means that images of the object or scene are required. Images must be of the highest quality in order to create an accurate model. Poor-quality images can significantly impact the accuracy and precision of the resulting 3D model [76; 77]. One factor that can significantly reduce model quality but can be controlled is motion blur. Motion blur is the apparent streaking of moving parts (relative to the frame) of the scene. It results due to the rapid movement of the scene or long exposure. Motion blur can make the features in an image less distinct and more difficult to identify, which can lead to modeling errors. It is very convenient to collect images of the object by using a smartphone to make a video of it. However, the video may contain a lot of relatively similar frames that do not increase model quality but slow down the reconstruction process. In order to get rid of unnecessary and lower-quality frames from the video, images can be compared according to their quality and the best images selected to be used in photogrammetry. In this case, reference-free image quality evaluation metrics are required [78; 79; 80]. This work proposes a method for detecting/choosing the least motion-blurred image. When we get images from a single video file, and we want to find an appropriate image for the photogrammetry, the varying quality-degrading factor is motion blur, so we perform motion blur evaluation in order to find the highest quality image in the set of images. The novelty and contributions of this work can be summarized as follows:  
* Proposed a method for a video frame quality evaluation by predicting motion blur level. The proposed method reuses early features of the convolutional neural network (CNN), which is used in the same task (head 3D reconstruction) for human head detection. * Presented a dataset construction method/guide for the development of motion blur evaluation method in video frames or images.