# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 2 Proposed System
### Modeling
#### 2.1.2 Generation of Adaptive Feature Pool  
Since, the focus of this paper is an online detection system for multiple objects, a single feature is insufficient. Therefore, we adopted and combined three different feature descriptors that have good performance in different kinds of images. There are Haar-like features, which can detect the change in grey level, HOG features, which are good at analyzing gradient direction, and LBP features, which can analyze the image texture. In the initializing part, the proposed system generates 1000 Haar-like, HOG, and LBP initial features each according to the aspect ratio of the labeled target patterns. We adopted six kinds of basic Haar-like features with random scales and positions. Figure 6 shows the basic Haar-like features that are used and the process of feature extraction. To use HOG and LBP features, we simplify them to increase the processing speed. The simplified  
Figure 4: Negative pattern. Figure 5: Makeup of the background in the first labeled frame. Figure 3: Positive pattern. method is similar to a Haar-like feature. After randomly generating the position and size of a rectangle in the training pattern, HOG calculates the statistics of the gradient value in this rectangle to derive a nine-dimensional histogram for feature extraction, as shown in Figure 7. LBP calculates the statistics of the LBP in this rectangle to derive a sixteen-dimensional histogram for feature extraction, as shown in Figure 8.  
After calculating the initial features, we used the difference between the average feature response value of the positive patterns and the negative patterns ( \(P_{mean}\) and \(N_{mean}\)) as the distinguishing ability of a feature from the existing patterns. We selected 1000 Haar-like, HOG, and LBP initial features randomly in this way. The total number of features of 1000 can be adjusted according to the calculation performance. In the experiment, using 1000 initial features for selection and replacement, all can achieve real-time calculations and maintain sufficient detection results. The number of dynamically selected features for each type are shown in (2)-(4). Finally, we adopted these initial features to compose the adaptive feature pool. Figure 6: Feature extraction process of Haar-like. Figure 7: Feature extraction process of HOG. Number of dynamic Haar-like features:  
\[\frac{|HaarP_{mean}-HaarN_{mean}|\times 1000}{|HaarP_{mean}-HaarN_{mean}|+|HOGP_{ mean}-HOGN_{mean}|+|LBPP_{mean}-LBPN_{mean}|} \tag{2}\]  
Number of dynamic HOG features:  
\[\frac{|HOGP_{mean}-HOGN_{mean}|\times 1000}{|HaarP_{mean}-HaarN_{mean}|+|HOGP_{ mean}-HOGN_{mean}|+|LBPP_{mean}-LBPN_{mean}|} \tag{3}\]  
Number of dynamic LBP features:  
\[\frac{|LBPP_{mean}-LBPN_{mean}|\times 1000}{|HaarP_{mean}-HaarN_{mean}|+|HOGP_{ mean}-HOGN_{mean}|+|LBPP_{mean}-LBPN_{mean}|} \tag{4}\]  
To evaluate the performance of the proposed adaptive feature pool, we used CMU PIE (face) [28], VOC 2005 Person (pedestrian) [29], and CarData (vehicle) [30] databases. In the experiment, we used the feature pool including 1000 features and adopted Adaboost [9] for feature selection. The termination condition was when the number of weak classifier reached 100 or the error rate approached zero (in this research, we set it as 0.00001). We performed the experiments three times for each database and used the average error rate for analysis. The result shown in Table 1 explains that it does not matter whether a single type of feature is used or combined with another; these features all achieved good performance on a single-class classification. However, the proposed adaptive feature pool showed the best performance with respect to adapting different kinds of objects.