# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 1 Introduction  
The importance of computer vision-related technology and research is increasing with the increasing efficiency of hardware computing. The main research areas in computer vision are object detection and classification techniques. Accurate detection and quick identification of objects has always been the most important issue in this area. Among all object detection methods, machine learning is one of the most frequently studied. In recent years, there have been many successful applications such as face detection and recognition [1, 2], vehicle detection and tracking [3, 4, 5], and pedestrian detection [6, 7, 8]. There many single-class offline learning algorithms in machine learning that use massive training data and algorithms to find an approximation function closest to the unknown target function. The most common ones are Adaptive Boosting [9] and Support Vector Machine [10]. These single-class offline learning algorithms usually require a person to collect and classify a large number of positive and negative samples, after which different feature extraction and selection methods are used to find the distinguishable features for the dataset. Finally, these features are combined into a detection model by using a machine learning algorithm. For multiple objects, it is necessary to train several detection models in the same way. Although single-class offline learning has performed well in the field of object detection, substantial training time is necessary for multi-object detection. Thus, it is not recommended to combine several single object models together. As a result, multi-class offline learning is proposed. Multi-class offline learning has many benefits, both in detection and classification. For example, detection speed and the discrimination of the multi-class offline learningmodel in classifying several objects usually perform better than the model built by multiple single-class offline learning models, as multi-class offline learning algorithms are obtained by combining or modifying single-class offline learning algorithms in a variety of ways. In addition, the algorithm, developed in this way, adapts to the multi-category classification problem. Some examples of such algorithms are Multi-class Support Vector Machine [11], Joint Boosting [12], and Random Forests [13]. Although these multi-class offline learning algorithms effectively solve the problem of multiple objects classification, people must collect and classify a large number of positive and negative samples in advance, thus requiring more memory space and training time. These problems could mean that the algorithm cannot be trained in real-time with their application in some specific situations. To solve these problems, an online learning algorithm is proposed in this paper. The online learning algorithm does not need to collect a large number of positive and negative samples in advance; instead, providing data one by one can make it adapt to the scene gradually. As a result, it does not need to learn the classifier at one time from a large number of samples. This not only reduces the training time of the detection model, but also decreases the memory space required. Most of these online learning algorithms evolved from offline learning algorithms. However, some of them also evolved from single-class to multi-class, like offline learning. [PERSON] and [PERSON] proposed Online Boosting [14], that is an improved online version of Adaptive Boosting [9], which achieves the effect of boosting by simulating the number of times the data are sampled. [PERSON] and [PERSON] proposed another Online Boosting method [15; 16], which simulates Adaptive Boosting [9] but executes in a single-step method. It mainly selects the better classifier from the selector and combines these classifiers into one strong classifier. It replaces the weak classifier in the selector according to the adaption degree of samples to update the model.