# Content-Based Video Big Data Retrieval with Extensive Features and Deep Learning
## 2 State of the Art Overview  
In recent years, many studies on video retrieval have been conducted. These studies have great improvements in accuracy and processing speed but are still limited in the diversity of content recognition and experimental datasets. [PERSON] et al. proposed a word detector from video input that did not require external knowledge sources for training and was trainable in an end-to-end manner jointly with any video-to-language models [8]. The proposal was demonstrated in the Large Scale Movie Description Challenge 2016. [PERSON] et al. addressed the problem of audio-based near-duplicate video retrieval by capturing temporal patterns of audio similarity between video pairs [9]. They used a pre-trained CNN on a large scale dataset of audio events and calculated the similarity matrix derived from the pairwise similarity of these descriptors. The experiments were conducted on three visual-based datasets, i.e., FIVR-200K [10], SVD [11], and EVVE [12]. In 2019, [PERSON] and [PERSON] applied deep learning to classify emotions based on music videos [13]. This system was tested on four unimodal and four multimodal neural networks and the best model had an accuracy of 88.56%. This method automatically classified human high-level emotions with relatively high performance. This study focused on processing musical video features in a traditional environment and did not consider distributed and parallel processing in a big data context. [PERSON] proposed a content-based video retrieval method with orthogonal polynomials [14]. This system identifies keyframes from the input images and uses the colors, textures, angles, and shapes of the visual content. These features are indexed for video retrieval. This method has only been tested on 20 videos, uses only visual attributes, and achieves an accuracy of 69%. This system was quite simple, but it ignored the audio characteristics contained in videos, thus, the performance is not good. [PERSON] et al. used an attention-based temporal weighted convolutional neural network (ATW CNN) to identify actions in video [15]. Experimental results on the UCF-101 [16] and HMDB-51 [17] datasets show that the recognition performance of relevant video segments using this model increases significantly from 55.9% to 94.6%. The method eliminated extra information that may cause noise on the image by using appropriate temporal weights to improve the efficiency of feature extraction. Therefore, it requires suitable weights to get better performance and does not consider the processing time. Many recent studies are looking for new approaches and methods of combining different techniques to provide a scientific basis for future research. [PERSON] et al. solved the problem of multi-person human pose estimation and tracking in videos with an approach consisting of three components, a Clip Tracking Network, a Video Tracking Pipeline, and a SpatialTemporal Merging procedure [18]. The experiments on PoseTrack 2017 and 2018 datasets achieve accuracy from 77.6% to 86.5%. This approach heavily relies on the object images to be identified, and cannot achieve good results with small objects. Recently, [PERSON] et al. introduced ViPNAS, an effective video pose estimation search in both spatial and temporal levels using ResNet-50 and MobileNet v3 as the backbone [19]. Experiments on the COCO2017 and PoseTrack2018 datasets provided high inference speeds without sacrificing accuracy. This work only focuses on image features, ignoring audio features that are also very important in video management. [PERSON] et al. performed automatic speech recognition with DeepSpeech models for improving the accuracy of Komi language recognition [20]. Their experiments with language models created using KenLM from text materials available online showed significant improvements of over 25% in character error rate and nearly 20% in word error rate. This gave an insight to improve ASR results under low-resource conditions, i.e., the lack of training data. [PERSON] et al.