# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Visual Based Data Representation and Event Detection
#### 4.2.2 Video Based Data Representation
Researchers have also studied high level representation for video based event detection. For example, [PERSON] et al. [67] proposed semantic model vector to detect events in unconstrained videos. The semantic model vector is an intermediate level semantic representation and is extracted using a set of discriminative semantic classifiers, each being an ensemble of SVM models. An end-to-end video event detection system was adopted and combined semantic model vectors with other static or dynamic visual descriptors, extracted at the frame, segment, or full clip level. [PERSON] [68] proposed to use a model vector-based approach, where visual concept detectors are used to automatically describe a video sequence in a concept space. In addition, there are some researchers who used high-level video representations with a set of low-level features for video event detection. For example, [PERSON] et al. [69] used the static features, dynamic features and the bag-of-word representations to represent action, scene and object concepts. With the development of deep learning, many researchers used deep convolutional neural networks (CNN) for computer visual tasks and has achieved great success in video processing [70]. For example, [PERSON] et al. [71] were the first to propose to use encoding techniques for video representation generation. Their proposed method is a discriminative video representation on large dataset under limited hardware resources. The representation effectively applied deep Convolutional Neural Networks (CNNs) to the event detection task. To enrich visual information, they proposed to use a set of latent concept descriptors as the frame descriptor. [PERSON] et al. [72] surveyed different methods for event detection and action recognition in videos by using convolutional neural networks (CNN). Results show that the CNN-based approach can outperform the static and motion-based Fisher vector techniques. What is more, integrating motion information with simple late fusion can improve classification performance. [PERSON] et al. [73] proposed a large scale event-specific concept library that covers many real-world events and concepts, which is called EventNet. They trained a Convolutional Neural Network (CNN) model to extract deep learning feature from video content. [PERSON] et al. [74] proposed a new multimodal deep learning framework for video event detection, which can automatically generate deep features from each modality. Besides the CNN, there also some recent works that used other deep learning methods. For example, [PERSON] et al. [75] proposed a new ensemble deep learning framework, which can be utilized in various scenarios and datasets. Their proposed method not only overcomes the imbalanced data issue, but also decreases the information loss and overfitting problems caused by single models. [PERSON] et al. [76] proposed the Joint Event Detection and Description Network (JEDDi-Net). The model utilized three-dimensional convolution to extract video appearance and motion features, which are sequentially passed to the temporal event proposal network and the captioning network. [PERSON] et al. [77] proposed a two stage neural network for the video event classification task, where the first stage can transfer pre-learned object knowledge to video contents and the second stage can combine temporal information by RNN. A summary of the representation of video based event detection methods is shown in Table 2.