| dc.description.abstract | With the rapid development of intelligent technologies, gesture recognition has become one of the most popular research areas in the world. It is the ability of a computer or smart device to detect and interpret human gestures. Such gestures, including movements of hand or body, facial expressions or even voice commands, can be used to control devices or interfaces. Air-writing is a new human and smart device communication approach which permits users to write inputs in a natural and relentless way. Gait recognition is another one for healthcare and surveillance. And machine learning can be applied to these two typical applications to analyze and interpret the captured data.
Compared with other writing methods, air-writing is more challenging due to its unique characteristics such as redundant lifting strokes, multiplicity, and confusion. Without using any starting trigger, we propose a novel reverse time-ordered algorithm to efficiently filter out unnecessary lifting strokes, and thus simplifies the matching procedure. Then a tiered arrangement structure is proposed by sampling the air-writing results with various sampling rates to solve the multiplicity and confusion problems. The recognition accuracy of the proposed approach is satisfactorily higher than 94%.
As to the gait recognition, we apply a deep neural network (DNN) to achieve gait-based automatic pedestrian detection and recognition. Instead of using wearable devices to precisely capture skeletal and joint movements, pedestrian color-image sequences are used as input. At a subsequent time, a pretraining convolutional neural network (CNN) is employed to capture pedestrian location, and the pedestrian dense optical flow is extracted to serve as concrete low-level feature inputs. Then, a finely-tuned DNN based on the wide residual network is employed to extract high-level abstract features. In addition, to overcome the difficulty of obtaining local temporal features by using a 2D CNN, part of the 3D convolutional structure is introduced into the CNN. This design enabled use of limited memory to acquire more effective features and enhance the DNN performance. The experimental results show that the proposed method has exceptional performance for pedestrian detection and recognition. | en_US |