An evaluation of streaming digital video resources in on-and off-campus engineering management education

A recent television documentary on the Columbia space shuttle disaster was converted to streaming digital video format for educational use by on- and off-campus students in an engineering management study unit examining issues in professional engineering ethics. An evaluation was conducted to assess the effectiveness of this new resource. Use of the video was optional, and about half of the class reported using the video, though usage was 90.0% for off-campus students. Most on-campus students accessed the video on-line, while all off-campus students accessed the video via CD-ROM. Off-campus students rated the educational value of the video higher than on-campus students, and were more likely to indicate that the video helped them understand the issues being studied. Most students were able to view the videos without any technical playback problems.

Video reflection and the formation of teacher identity in a team of pre-service and experienced teachers

This paper discusses a project in which a team of pre-service and experienced teachers, following a teaching development model devised by the authors, reflected on videos of the pre-service teachers teaching literacy. Using a discourse analytic approach, the paper focuses on how teachers' joint reflection contributes to student teacher identity formation. Analysis suggests that reflection, at least in the talk of this team, is a language practice with a distinctive generic structure. Using this structure, participants jointly construct professional teacher identities for themselves and others through the key devices of representation, categorization, evaluation, individualization and inclusion.

Distance-driven fusion of gait and face for human identification in video

Gait and face are two important biometrics for human identification. Complementary properties of these two biometrics suggest fusion of them. The relationship between gait and face in the fusion is affected by the subject-to-camera distance. On the one hand, gait is a suitable biometric trait for human recognition at a distance. On the other hand, face recognition is more reliable when the subject is close to the camera. This paper proposes an adaptive fusion method called distance-driven fusion to combine gait and face for human identification in video. Rather than predefined fixed fusion rules, distance-driven fusion dynamically adjusts its rule according to the subject-to-camera distance in real time. Experimental results show that distance-driven fusion performs better than not only single biometric, but also the conventional
static fusion rules including MEAN, PRODUCT, MIN, and MAX.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Multi-level semantic analysis for sports video

There has been a huge increase in the utilization of video as one of the most preferred type of media due to its content richness for many significant applications including sports. To sustain an ongoing rapid growth of sports video, there is an emerging demand for a sophisticated content-based indexing system. Users recall video contents in a high-level abstraction while video is generally stored as an arbitrary sequence of audio-visual tracks. To bridge this gap, this paper will demonstrate the use of domain knowledge and characteristics to design the extraction of high-level concepts directly from audio-visual features. In particular, we propose a multi-level semantic analysis framework to optimize the sharing of domain characteristics.