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– This paper introduces a new statistical framework, namely Tensor Canonical Correlation Analysis (TCCA) which is an extension of classical Canoni-cal Correlation Analysis (CCA) to multidimensional data arrays also called tensors. We apply this method to inspect multidimensional (joint space-time) linear relationships of two videos for human action and(More)
Current approaches to motion category recognition typically focus on either full spatiotemporal volume analysis (holistic approach) or analysis of the content of spatiotem-poral interest points (part-based approach). Holistic approaches tend to be more sensitive to noise e.g. geometric variations, while part-based approaches usually ignore structural(More)
Local spatiotemporal features or interest points provide compact but descriptive representations for efficient video analysis and motion recognition. Current local feature extraction approaches involve either local filtering or entropy computation which ignore global information (e.g. large blobs of moving pixels) in video inputs. This paper presents a(More)
This paper presents a new incremental learning solution for Linear Discriminant Analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to(More)
An approach to recognise and segment 9 elementary gestures from a video input is proposed and it can be applied to continuous sign recognition. An isolated gesture is recog-nised by first converting a portion of video into a motion gradient orientation image and then classifying it into one of the 9 gestures by a sparse Bayesian classifier. The portion of(More)
An approach to recognise 10 elementary gestures is proposed and it can be applied to sign language recognition. In this work, a motion gradient orientation image is extracted directly from a raw video input and transformed to a motion feature vector. This feature vector is then classified into one of the 10 elementary gestures by a sparse Bayesian(More)
An approach to increase adaptability of a recognition system, which can recognise 10 elementary gestures and be extended to sign language recognition, is proposed. In this work, recognition is done by firstly extracting a motion gradient orientation image from a raw video input and then classifying a feature vector generated from this image to one of the 10(More)
An appearance-based approach to track an object that may undergo appearance change is proposed. Unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. Through the use of a sparse Bayesian classifier, high classification and detection accuracy can be(More)