Continuous Action Recognition Based on Sequence Alignment

Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping (DTW) framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods

Improving face gender classification by adding deliberately misaligned faces to the training data

A novel method of face gender classifier construction is proposed and evaluated. Previously, researchers have assumed that a computationally expensive face alignment step (in which the face image is transformed so that facial landmarks such as the eyes, nose, chin, etc, are in uniform locations in the image) is required in order to maximize the accuracy of predictions on new face images. We, however, argue that this step is not necessary, and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned (for example, translated or rotated). To test our hypothesis, we evaluate this automatic training dataset expansion method with two types of image classifier, the first based on weak features such as Local Binary Pattern histograms, and the second based on SIFT keypoints. Using a benchmark face gender classification dataset recently proposed in the literature, we obtain a state-of-the-art accuracy of 92.5%, thus validating our approach

Path, theme and narrative in open plan exhibition settings

Three arguments are made based on the analysis of science exhibitions. First,sufficiently refined techniques of spatial analysis allow us to model the impact oflayout upon visitors' paths, even in moderately sized open plans which allow almostrandom patterns of movement and relatively unobstructed visibility. Second, newlydeveloped or adapted techniques of analysis allow us to make a transition frommodeling the mechanics of spatial movement (the way in which movement is affectedby the distribution of obstacles and boundaries), to modeling the manner in whichmovement might register additional aspects of visual information. Third, theadvantages of such purely spatial modes of analysis extend into providing us with asharper understanding of some of the pragmatic constrains within which exhibitioncontent is conceived and designed

Path, theme and narrative in open plan exhibition settings

Three arguments are made based on the analysis of science exhibitions. First,sufficiently refined techniques of spatial analysis allow us to model the impact oflayout upon visitors' paths, even in moderately sized open plans which allow almostrandom patterns of movement and relatively unobstructed visibility. Second, newlydeveloped or adapted techniques of analysis allow us to make a transition frommodeling the mechanics of spatial movement (the way in which movement is affectedby the distribution of obstacles and boundaries), to modeling the manner in whichmovement might register additional aspects of visual information. Third, theadvantages of such purely spatial modes of analysis extend into providing us with asharper understanding of some of the pragmatic constrains within which exhibitioncontent is conceived and designed

Automatic parsing of sports videos with grammars

Motivated by the analogies between languages and sports videos, we introduce a novel approach for video parsing with grammars. It utilizes compiler techniques for integrating both semantic annotation and syntactic analysis to generate a semantic index of events and a table of content for a given sports video. The video sequence is first segmented and annotated by event detection with domain knowledge. A grammar-based parser is then used to identify the structure of the video content. Meanwhile, facilities for error handling are introduced which are particularly useful when the results of automatic parsing need to be adjusted. As a case study, we have developed a system for video parsing in the particular domain of TV diving programs. Experimental results indicate the proposed approach is effectiv

A New Multi-threaded and Interleaving Approach to Enhance String Matching for Intrusion Detection Systems

String matching algorithms are computationally intensive operations in computer science. The algorithms find the occurrences of one or more strings patterns in a larger string or text. String matching algorithms are important for network security, biomedical applications, Web search, and social networks. Nowadays, the high network speeds and large storage capacity put a high requirement on string matching methods to perform the task in a short time. Traditionally, Aho-Corasick algorithm, which is used to find the string matches, is executed sequentially. In this paper, a new multi-threaded and interleaving approach of Aho-Corasick using graphics processing units (GPUs) is designed and implemented to achieve high-speed string matching. Compute Unified Device Architecture (CUDA) programming language is used to implement the proposed parallel version. Experimental results show that our approach achieves more than 5X speedup over the sequential and other parallel implementations. Hence, a wide range of applications can benefit from our solution to perform string matching faster than ever before