Abstract
A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Highlights
The importance of computer vision-related technology and research is increasing with the increasing efficiency of hardware computing
A good solution would be to combine these learning algorithms with an external model according to the status of the model after learning to increase the detection performance of the model
The results demonstrated the performance of our online learning algorithm
Summary
The importance of computer vision-related technology and research is increasing with the increasing efficiency of hardware computing. 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 These features are combined into a detection model by using a machine learning algorithm. Some examples of such algorithms are Multi-class Support Vector Machine [11], Joint Boosting [12], and Random Forests [13] 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, requiring more memory space and training time. The system is able to adjust the model according to all pattern pools so that it is not affected by previous false decisions
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