Mustafa Fanaswala

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On meta-level time scales, anomalous trajectories can signify target intent through their shape and eventual destination. Such trajectories exhibit complex spatial patterns and have well defined destinations with long-range dependencies implying that Markov (random-walk) models are unsuitable. How can estimated target tracks be used to detect anomalous(More)
We consider the prediction of a target's destination and simultaneously recover its filtered trajectory. Two novel models for trajectories with known destinations are presented using reciprocal stochastic processes and stochastic context-free grammars. We present a destination-aware syntactic tracker which uses conventional state-space estimates from a(More)
In this paper, a track before detect approach utilizing trajectory shape constraints is proposed to track dimly lit targets. The shape of the target trajectory is modeled syntactically using stochastic context-free grammar models (SCFG) that arise in natural language processing. The directional vector of the target acceleration modes are used as geometric(More)
This paper presents generalized models for characterizing spatio-temporal target trajectories that have anomalous patterns. Stochastic context-free grammars (SCFGs) are the modeling framework used to represent anomalous events like circling behaviors and destination-specific trajectories. We propose a hierarchical tracking architecture to ensure legacy(More)
a r t i c l e i n f o a b s t r a c t This paper considers the intent inference problem in a video surveillance application involving person tracking. The video surveillance is carried out through a distributed network of cameras with large non-overlapping areas. The target dynamics are formulated using a novel grammar-modulated state space model. We(More)
In this paper, a track before detect approach utilizing trajectory shape constraints is proposed to track dimly lit targets. The shape of the target trajectory is modeled syntactically using stochastic context-free grammar (SCFG) models that arise in natural language processing. These scale-invariant models are subsequently used in enhancing the track(More)
In this paper, a novel mode-driven switching state space approach is proposed for the joint tracking and recognition of gestural commands. Gestures are modeled as spatio-temporal patterns comprised of syntactic sub-units called gesturelets. These gesturelets are directional vectors modulating a switching state space model. Stochastic context-free grammars(More)
Abstract—The medical community has expressed a strong need for developing non-invasive techniques of monitoring the fetal electrocardiogram (ECG) which is vital for prenatal diagnostics. The dynamic environment of the human body and associated interfering signals lends itself to the adoption of an adaptive technique for fetal ECG extraction. The objective(More)
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