View-invariant motion trajectory-based activity classification and recognition
Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.
activity recognition curvature scale space centroid distance function hidden markov models affine-invariant trajectory descriptors trajectory modeling