# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 2 Related Works
They investigated the performance using majority voting (MV) and logarithmic opinion pool (LOGP) fusion strategies. Likewise, [PERSON] et al. [37] projected the depth maps into three orthogonal Cartesian planes and generated three DMMs along the front, side, and top views. They used an l2-regularized classifier with a distance-weighted Tikhonov matrix for action discrimination. [PERSON] et al. [38] partitioned a whole depth sequence into sub-sequences with uniform length, called vague division (VD), and projected them onto three orthogonal planes. They also computed DMMs by determining the difference between adjacent frames of the projected views. [PERSON] et al. [39] sampled the entire depth sequences based on the motion energy of key-frames and produced a weighted depth motion map (WDMM). From the WDMM, they extracted HOG and LBP features to perform the classification. [PERSON] et al. [40] extended the DMMs-based human action recognition in which they extracted local ternary pattern features to filter the DMMs and conducted a CNN model for classification. [PERSON] et al. [41] segmented a depth sequence to sub-actions called energy-guided sub-actions (EGSA) and time-guided sub-action (TGSA) to explore the sub-actions relationship for action recognition. [PERSON] et al. [42] used a multi-scale feature map called Laplacian pyramid depth motion images (LP-DMI) obtained from DMI. Finally, they conducted an extreme learning machine (ELM) for action recognition using the features extracted with HOG and visual geometry group (VGG) descriptors. Similarly, [PERSON] et al. [43] split a depth sequence into two different sizes of sub-sequences and computed several DMMs. From the obtained DMMs, they extracted auto-correlation of gradient features (ACG) and classified them using an l2-regularized collaborative representation classifier (CRC). [PERSON] et al. [44] proposed a new algorithm called self-adaptive differential evolution with a knowledge-based control parameter-extreme learning machine. They calculated the LBP features from DMM and evaluated the recognition results using CRC, probability CRC, and kernel ELM with LBP features. The performance of the above methods is vastly dependent on the effective representation of depth sequence into the skeleton, point cloud, and dynamic image. When the number of frames and position of the camera or human varies, the spatial and the temporal information of the same action can differ and degrade the overall performance. Moreover, it is very cumbersome and time-consuming to convert the depth sequence into the skeleton, point cloud, and dynamic image and extract discriminative features from them. Therefore, some methods have used raw depth sequence to recognize human action. [PERSON] et al. [45] designed a view correlation discovery network (VCDN) to generate high-level information by fusing multi-modality information. [PERSON] et al. [46] proposed a 3D-CNN model to automatically extract spatial and temporal information from the raw depth sequence. In [47], [PERSON] et al. introduced the view-correlation adaptation (VCA) technique to handle multi-modality datasets for action recognition. They integrated a view encoder which extracted the view representation features from the depth and RGB sequences. They evaluated the proposed method with different features representation, such as VCA-Entropy and semi-supervised feature augmentation (SeMix). [PERSON] et al. [48] proposed a new method for human action recognition with raw depth sequences. They suggested a collaborative attention mechanism (CAM) to solve the multi-view problems. To obtain the multi-view collaborative process, they conducted an extended LSTM model. As described earlier [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], the encoding of depth sequences into other formats such as skeleton, point cloud, and dynamic required additional effort and time. As a result, it is a good idea to use the raw data directly to recognize the action.