# Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition
## 2 Literature Review  
The key goal of the proposed method is the pre-processing of input videos in order to remove unnecessary information at the beginning of the HAR system, so this section is mainly focused on keyframe selection and ROI finding at the input block of HAR systems. The existing methods for keyframe selection can be classified into the following groups: methods based on extracting temporal or spatial features, methods based on deep learning and hybrid methods. In all of these categories, the efficiency of the method is mainly defined based on the application. The methods based on extracting temporal or spatial features use saliency features, such as edge or motion features, for keyframe selection. [PERSON] et al. [9] used the Laplacian operator to select the appropriate sequences for short periods as the input of a deep learning scheme to identify Hong Kong sign language. [PERSON] and [PERSON] [10] performed elastomeric experiments using ultrasonic images taken from a pressurized mechanical object. The acquired images were first submitted to a multilayer perceptron (MLP) classifier, and any two consecutive frames that contain no relevant information are removed from the list of training frames. They decomposed the displacement into a linear combination of weighted principal components, which were used as an input for the MLP. [PERSON] and [PERSON] [11] proposed a method for selecting keyframes in RGB-Depth (RGB-D) video tracking systems, which uses the difference of frames and features extracted from the image depth information simultaneously. [PERSON] et al. [12] used the Kanade-Lucas-Tomasi (KLT) algorithm to select keyframes in an automated driving system. In this method, the main features, such as margins, road lines and other obstacles, are extracted from the frames, and then the difference between these features is used to discard or keep the frames. [PERSON] et al. [13] used a fuzzy method based on single-frame information, such as contrast, edges and luminescence, in order to select the best frames for watermarking. [PERSON] et al. [14] suggested a frame selection scheme for video-based person re-identification. The spatial and temporal characteristics are used simultaneously to select keyframes. None of the above research was designed for HAR systems, and none of them can be applied directly to a HAR system, but they have features and concepts that can be used in the development of a suitable method. The second category includes methods based on deep learning; therefore, these methods use a type of deep learning such as long short-term memory (LSTM) and the convolutional neural network (CNN) for keyframe selection. [PERSON] et al. [15] proposed a method using an autoencoder for selecting the frame sequence of a movie to create an automatic thumbnail for the video, which is vital for, as an example, online video sharing sites that provide a short tag for each movie. [PERSON] et al. [16] proposed a dynamic method to remove unnecessary video information for video recognition. The authors used an LSTM network for selecting frames with the most relevant information. [PERSON] and [PERSON] [17] trained various CNN-based methods, such as ResNet, to select frames to create video thumbnails automatically. [PERSON] et al. [18] proposed a hybrid visual tracking method based on deep and reinforcement learning. The suggested method only used frames where the object being tracked moved away from its previous location more than a specific threshold; the other frames were detected and removed from the training process. [PERSON] et al. [19] used the ConvNet network with two different spatial and temporal approaches in order to select keyframes in a HAR system. The Xiang method maintains action consistency and selects frames with more spatial and temporal information as an input. [PERSON] et al. [20] used the LSTM to select keyframes in video recognition. The suggested method increases the training speed, reduces the frame length and improves the network performance.