Abstract-Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan-tilt-zoom traffic camera monitoring the junction of two busy intersecting highways.

This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations -least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a SelfOrganising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasetsAustralian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. Object trajectories are modeled as motion time series using the lowest order Fourier coefficients obtained by Discrete Fourier Transform. Trajectory clustering is then carried out in the DFT-coefficient feature space to discover patterns of similar object motion activity. The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a simple Mahalanobis classifier for the detection of anomalous trajectories. Our proposed techniques are validated on three different datasets -Australian sign language, handlabelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for visual surveillance systems are envisaged.

This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets -synthetic and pedestrian object tracking -to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.

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In this paper, a semi-automated approach to 3-D landmark digitization of the face is described which uses a combination of active shape model-driven feature detection and stereophotogrammetric analysis. The study aims to assess whether the proposed method is capable of detecting statistically significant changes in facial soft tissue shape due to mandibular repositioning in a cross-sectional patient sample. A hybrid stereophotogrammetric and structured-light imaging system is used for acquiring 3-D face models in the first instance. A landmark-based statistical analysis of facial shape change is then carried out using procrustes registration, principal component analysis and thin plate spline warping on the 2-D facial midline profiles and automatically digitized 3-D landmarks. The proposed method is validated both statistically and visually by characterizing shape changes induced by mandibular repositioning in a heterogeneous cross-sample of 20 orthodontic patients. It is shown that the method is capable of distinguishing between changes in facial morphology due to simulated surgical correction and changes due to other factors such as growth and normal variation within the patient sample. The study shows that the proposed method may be useful for auditing outcomes of clinical treatment or surgical intervention which result in changes to facial soft tissue morphology.