Video analysis for human action recognition is one of the most important research areas in pattern recognition and computer vision due to its wide applications. Deep learning-based approaches have been proven more effective than conventional feature engineering-based models. However, the performance is still unreliable when facing real-world application scenarios. Inspired by the Convolutional Neural Network (CNN) and Recurrent Long-Short Term Model (LSTM), this paper presents an augmented treble-stream deep neural network architecture that supports direct extraction of spatial-temporal features from video streams and their corresponding dense optical flows. This innovative approach assists effective detection of complex video event features that are annotated by rich event "appearance" and motion features. Substantially improved recognition accuracy is recorded during the experiments that are carried and benchmarked over public video event datasets, for example, UCF 101 and HMDB 51. Analytical evaluation approves the validity and effectiveness of the treble-stream neural network design.

A measurement of head-related transfer functions (HRTFs) with high spatial resolution was carried out in this study. HRTF measurement is difficult in the proximal region because of the lack of an appropriate acoustic point source. In this paper, a modified spark gap was used as the acoustic sound source. Our evaluation experiments showed that the spark gap was more like an acoustic point source than others previously used from the viewpoints of frequency response, directivity, power attenuation, and stability. Using this spark gap, high spatial resolution HRTFs were measured at 6344 spatial points, with distances from 20 to 160 cm, elevations from -40deg to 90deg, and azimuths from 0deg to 360deg. Based on these measurements, an HRTF database was obtained and its reliability was confirmed by both objective and subjective evaluations.