# A Distributed Automatic Video Annotation Platform
## 2 Related Work  
Currently, video annotation is one of the most fascinating topics among researchers, since it plays a vital role in the knowledge-based system (KBS) for generating knowledge by describing the content and essential video information; therefore, researchers have done a lot of work in this area. This section presents the literature review for video annotation.  
Recent works [16; 17; 18; 19] tried to exploit the contextual information from an image by detecting the object and scene information. YoloV3 [16] provided the fastest annotation techniques; they trained their network on deep convolutional neural network Darknet19 using image datasets COCO and PASCAL. Although all of these methods provided state-of-the-art object detection performance, they focusedon tagging the objects only. Annotation inherits the property of object detection and uses the concept of semantic image information by giving the textual context. [PERSON] et al. [20], divided automatic image annotation (AIA) into five categories according to the work done previously; model building, accuracy, and computational time were the ultimate targets for automatic video annotation (AVA). Among these techniques, the nearest neighbor approach is very popular. The author in [21; 22] explored the automatic image annotation using the nearest neighbor technique by detecting the object that the image contained. However, video annotation is different, since the video contains spatiotemporal information and redundant spatial information.  
Existing video annotation can be classified into: visual context annotation and semantic context annotation. In the spatial context annotation, researchers have exploited the natural property from each frame of a video. Visual context is assigned to each frame of a video in [7; 8; 23; 24; 25; 26; 27; 28; 29]. Here the authors in [26] exploited the multi-level visual context from each frame of a video using the nearest neighbor approach. Similarly, in [30; 31] the authors explored the concept from a video and provided multi-level annotation. Later on, tree-based techniques were used in [32] to detect the duplicated video using scene annotation.  
However, all of these techniques lack the semantic information. Since a video contains more than visual information, several works [4; 5; 33; 34; 35; 36] have been done to analyze the semantic information from a video. These works explore the semantic information from each frame of a video and tag the predefined label that belongs to the frame.  
However, all of these works miss the temporal information of a video. Time-based information provides essential information about an event, such as accidents and cycling. In order to capture the temporal information, [34; 37] provided an automatic video annotation technique that keeps the temporal information, including the visual-spatial information. To find the temporal information over the sequential frame of a video the author in [34] used the association rules to correlate the spatial and temporal concepts from consecutive frames. On the other hand, in [37] the author proposed a spatiotemporal-based annotation to get the position of an object from several frames. However, all of those works did not consider the event information. Furthermore, they did not support distributed processing.  
In contrast, the authors in [38] provided a web-based video annotation platform that can annotate plain video information, such as visual information. In [39] a distributed framework for activity recognition from mobile data was presented. However, they did not consider the image and video information.  
In order to solve the aforementioned issues, we introduce a distributed spatiotemporal-based automatic video annotation platform that provides both the spatial information and the temporal information. Moreover, we provide distributed video annotation APIs for the developer and a video annotation service for the end-users.