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The design and implementation of a human detection module on mobile platform is described in this paper. A proof of concept system is developed using ARM processors with good mobile compatibility and low power consumption. The resultant design is shown to perform satisfactorily in several environments despite lower processing power and limited memory blocks. The optimization of some design parameters is also illustrated.

There is a chicken-and-egg problem in classification whereby a good classifier is required to test the efficacy of the features, yet a good feature set is required to generate a good classifier. When the salient features are unknown, an extremely large set of features is used to train the classifier in hopes of obtaining accurate classification results. This research proposes the use of a special class of decision tree called the alternating decision tree or ADTree to answer two questions in knowledge discovery in order to effectively select a salient feature set: When using a particular feature extraction algorithm, which of the features is able to distinguish between the different classes? And how do they work?