Incremental Learning and Online-Style SVM for Traffic Light Classification

Liu, Wen

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Incremental Learning and Online-Style SVM for Traffic Light Classification

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Training a large dataset has become a serious issue for researchers because it requires large memories and can take a long time for computing. People are trying to process large scale dataset not only by changing programming model, such as using MapReduce and Hadoop, but also by designing new algorithms that can retain performance with less complexity and runtime. In this thesis, we present implementations of incremental learning and online learning methods to classify a large traffic light dataset for traffic light recognition. The introduction part includes the concepts and related works of incremental learning and online learning. The main algorithm is a modification of IMORL incremental learning model to enhance its performance over the learning process of our application. Then we briefly discuss how the traffic light recognition algorithm works and the problem we encounter during training. Rather than focusing on incremental learning, which uses batch to batch data during training procedure, we introduce Pegasos, an online style primal gradient-based support vector machine method. The performance of Pegasos for classification is extraordinary and the number of instances it uses for training is relatively small. Therefore, Pegasos is the recommended solution to the large dataset training problem.

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