While various online multi-target tracking methods have been proposed recently, most of their runtime speed struggle at 1-10 frames per second for moderate crowded scenes. In this thesis, we present a novel real-time multi-target tracking system based on the tracking-by-detection framework. Our system is designed for tracking a variable number of interacting targets from a single, static, above shoulder camera, which is a general setting for video surveillance. One challenge in our approach is that when background subtraction is used for detecting moving targets, merged measurements occur frequently because of target interactions. To cope with the problem, we propose to use correlation filter based object detector to robustly separate the targets in merged measurements. Then, online object tracking assisted data association is used to solve the track-measurement assignment. To reduce computation load, our object tracking algorithm is assisted by correlations filter based trackers which share the same features used by our object detector. In addition, to recover partially occluded targets, we allow unconfident detections to be assigned to tracks whilst care is taken to avoid introducing additional false positives. We also analyze the online approximation to multi-channel correlation filters. Our experiments show that exact solution is more resistant to noisy channels than approximate solution. Evaluation on generally accepted datasets reveals that the proposed system is comparable to state-of-the-art methods in terms of performance while running several magnitudes faster. Additionally, we show that the proposed system can be readily implemented on the Texas Instruments TMS320C6678 DSP (C6678) without significant degradation in speed or performance. Details on efficient implementation of the system is also discussed. Especially, for computing Histogram of Oriented Gradients (HOG) feature, our optimized implementation runs at 60fps on VGA images on a single core of C6678, which is 10 times faster than a directly ported implementation. / published_or_final_version / Computer Science / Master / Master of Philosophy

This thesis examines whether it is possible for a machine to incrementally build a complex model of its environment, and then use this model for control purposes. Given a sequence of noisy observations, the machine forms a piecewise linear approximation to the nonlinear dynamic equations that are assumed to describe the real world. A number of existing online system identification techniques are examined, but it is found that they all either scale poorly with dimensionality, have a number of parameters that make them difficult to apply, or do not learn sufficiently accurate approximations. Therefore a novel framework is developed for learning linear model trees in both batch and online settings. The algorithms are evaluated empirically on a number of commonly used benchmark datasets, a simple test function, and three dynamic domains ranging from a simple pendulum to a complex flight simulator. The new batch algorithm is compared with three state-of-the-art algorithms and is seen to perform favourably overall. The new incremental model tree learner also compares well with a recent online function approximator from the literature. Armed with a tool for effectively constructing piecewise linear models of the environment, a control framework is developed that learns trajectories from a demonstrator and attempts to follow these trajectories within each linear region usinglinear quadratic control. The induced controllers are able to swing up and balance a simple forced pendulum both in simulation and in the real world. They can also swing up and balance a real double pendulum. The induced controllers are empirically shown to perform better than the original demonstrator, and could therefore be used to either replace a human operator or improve upon an existing automatic controller. In addition an ability to generalise the learnt trajectories enables the system to perform novel tasks. This is demonstrated on a flight simulator where, having observed an aircraft flying several times around a circuit, the controller is able to copy the take-off procedure, fly a completely new circuit that includes new manoeuvres, and successfully land the plane.

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis examines whether it is possible for a machine to incrementally build a complex model of its environment, and then use this model for control purposes. Given a sequence of noisy observations, the machine forms a piecewise linear approximation to the nonlinear dynamic equations that are assumed to describe the real world. A number of existing online system identification techniques are examined, but it is found that they all either scale poorly with dimensionality, have a number of parameters that make them difficult to apply, or do not learn sufficiently accurate approximations. Therefore a novel framework is developed for learning linear model trees in both batch and online settings. The algorithms are evaluated empirically on a number of commonly used benchmark datasets, a simple test function, and three dynamic domains ranging from a simple pendulum to a complex flight simulator. The new batch algorithm is compared with three state-of-the-art algorithms and is seen to perform favourably overall. The new incremental model tree learner also compares well with a recent online function approximator from the literature. Armed with a tool for effectively constructing piecewise linear models of the environment, a control framework is developed that learns trajectories from a demonstrator and attempts to follow these trajectories within each linear region usinglinear quadratic control. The induced controllers are able to swing up and balance a simple forced pendulum both in simulation and in the real world. They can also swing up and balance a real double pendulum. The induced controllers are empirically shown to perform better than the original demonstrator, and could therefore be used to either replace a human operator or improve upon an existing automatic controller. In addition an ability to generalise the learnt trajectories enables the system to perform novel tasks. This is demonstrated on a flight simulator where, having observed an aircraft flying several times around a circuit, the controller is able to copy the take-off procedure, fly a completely new circuit that includes new manoeuvres, and successfully land the plane.

Thesis (M.S.)--Ohio University, June, 1981. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, March, 1994. / Title from PDF t.p.