Pedestrian detection and identification

People are the centre of technologies. Understanding, monitoring and tracking the behaviour of people will benefit in various areas including driving assistance, surveillance for safety and caring purposes and applications for machine-people interaction. Particularly, pedestrians attract more attention for two reasons: they restrict the behaviours of people to standing and moving upright; and applications for pedestrian detection and monitoring have positively impact on the quality of life. Pedestrian detection and identification, aims at recognising pedestrians fromstill images and video frames. Together with pedestrian recognition and tracking, this topic attempts to train computers to recognise a pedestrian. The problem is challenging. Though frameworks were designed, various algorithms were proposed in recent years, further efforts are needed to improve the accuracy and reliability of the performance. In this thesis, proposing a modifiable framework for pedestrian identification and improving the performances of current pedestrian detection techniques are particularly focused. Based on appearance based pedestrian identification, a modifiable framework is a novel philosophy of developing frameworks which can be easily improved. For pedestrian identification, a novel protocol where layers of algorithms are hierarchically applied to solve the problem. To compare the detected pedestrians, appearance based features are selected, the "bag-of-features" framework is employed to compare the histogram descriptions of pedestrians. To improve the performances of HOG pedestrian detector, the presence of head-shoulder structure is selected as the evidence of the presence of pedestrian. A novel appearance based framework is developed to detect the head-shoulder structure from the detection results of HOG detector. Furthermore, in order to separate multiple pedestrians detected in one bounding box, a novel algorithm is proposed to detect the approximated symmetry axes of pedestrians.Open Acces