Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition

Historically, researchers in the field have spent a great deal of effort to create image representations that have scale invariance and retain spatial location information. This paper proposes to encode equivalent temporal characteristics in video representations for action recognition. To achieve temporal scale invariance, we develop a method called temporal scale pyramid (TSP). To encode temporal information, we present and compare two methods called temporal extension descriptor (TED) and temporal division pyramid (TDP) . Our purpose is to suggest solutions for matching complex actions that have large variation in velocity and appearance, which is missing from most current action representations. The experimental results on four benchmark datasets, UCF50, HMDB51, Hollywood2 and Olympic Sports, support our approach and significantly outperform state-of-the-art methods. Most noticeably, we achieve 65.0% mean accuracy and 68.2% mean average precision on the challenging HMDB51 and Hollywood2 datasets which constitutes an absolute improvement over the state-of-the-art by 7.8% and 3.9%, respectively

Analysis of some aerodynamic characteristics due to wing-jet interaction

The results of two separate theoretical investigations are presented. A program was used which is capable of predicting the aerodynamic characteristics of both upper-surface blowing (USB) and over-wing blowing (OWB) configurations. A theoretical analysis of the effects of over-wing blowing jets on the induced drag of a 50 deg sweep back wing was developed. Experiments showed net drag reductions associated with the well known lift enhancement due to over-wing blowing. The mechanisms through which this drag reduction is brought about are presented. Both jet entrainment and the so called wing-jet interaction play important roles in this process. The effects of a rectangular upper-surface blowing jet were examined for a wide variety of planforms. The isolated effects of wing taper, sweep, and aspect ratio variations on the incremental lift due to blowing are presented. The effects of wing taper ratio and sweep angle were found to be especially important parameters when considering the relative levels of incremental lift produced by an upper-surface blowing configuration

Investigation of empennage buffeting

Theoretical methods of predicting aircraft buffeting are reviewed. For the buffeting due to leading-edge vortex breakdown, a method is developed to convert test data of mean square values of fluctuating normal force to buffeting vortex strength through an unsteady lifting-surface theory and unsteady suction analogy. The resulting buffeting vortex from the leading-edge extension of an F-18 configuration is used to generate a fluctuating flow field which produces unsteady pressure distribution on the vertical tails. The root mean square values of root bending moment on the vertical tails are calculated for a rigid configuration. Results from a flow visualization and hot films study in a water tunnel facility using a 1/48 scale model of an F-18 are included in an appendix. The results confirm that the LEX vortex is the dominant forcing function of fin buffet at high angles of attack

Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Strea

Realistic interpretation of a superposition state does not imply a mixture

Contrary to previous claims, it is shown that, for an ensemble of either single-particle systems or multi-particle systems, the realistic interpretation of a superposition state that mathematically describes the ensemble does not imply that the ensemble is a mixture. Therefore it cannot be argued that the realistic interpretation is wrong on the basis that some predictions derived from the mixture are different from the corresponding predictions derived from the superposition state