Self-correcting Bayesian target tracking

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorAbstract Visual tracking, a building block for many applications, has challenges such as occlusions,illumination changes, background clutter and variable motion dynamics that may degrade the tracking performance and are likely to cause failures. In this thesis, we propose Track-Evaluate-Correct framework (self-correlation) for existing trackers in order to achieve a robust tracking. For a tracker in the framework, we embed an evaluation block to check the status of tracking quality and a correction block to avoid upcoming failures or to recover from failures. We present a generic representation and formulation of the self-correcting tracking for Bayesian trackers using a Dynamic Bayesian Network (DBN). The self-correcting tracking is done similarly to a selfaware system where parameters are tuned in the model or different models are fused or selected in a piece-wise way in order to deal with tracking challenges and failures. In the DBN model representation, the parameter tuning, fusion and model selection are done based on evaluation and correction variables that correspond to the evaluation and correction, respectively. The inferences of variables in the DBN model are used to explain the operation of self-correcting tracking. The specific contributions under the generic self-correcting framework are correlation-based selfcorrecting tracking for an extended object with model points and tracker-level fusion as described below. For improving the probabilistic tracking of extended object with a set of model points, we use Track-Evaluate-Correct framework in order to achieve self-correcting tracking. The framework combines the tracker with an on-line performance measure and a correction technique. We correlate model point trajectories to improve on-line the accuracy of a failed or an uncertain tracker. A model point tracker gets assistance from neighbouring trackers whenever degradation in its performance is detected using the on-line performance measure. The correction of the model point state is based on the correlation information from the states of other trackers. Partial Least Square regression is used to model the correlation of point tracker states from short windowed trajectories adaptively. Experimental results on data obtained from optical motion capture systems show the improvement in tracking performance of the proposed framework compared to the baseline tracker and other state-of-the-art trackers. The proposed framework allows appropriate re-initialisation of local trackers to recover from failures that are caused by clutter and missed detections in the motion capture data. Finally, we propose a tracker-level fusion framework to obtain self-correcting tracking. The fusion framework combines trackers addressing different tracking challenges to improve the overall performance. As a novelty of the proposed framework, we include an online performance measure to identify the track quality level of each tracker to guide the fusion. The trackers in the framework assist each other based on appropriate mixing of the prior states. Moreover, the track quality level is used to update the target appearance model. We demonstrate the framework with two Bayesian trackers on video sequences with various challenges and show its robustness compared to the independent use of the trackers used in the framework, and also compared to other state-of-the-art trackers. The appropriate online performance measure based appearance model update and prior mixing on trackers allows the proposed framework to deal with tracking challenges

A New Hardware Correlator in Korea: Performance Evaluation using KVN observations

We report results of the performance evaluation of a new hardware correlator in Korea, the Daejeon correlator, developed by the Korea Astronomy and Space Science Institute (KASI) and the National Astronomical Observatory of Japan (NAOJ). We conducted Very Long Baseline Interferometry (VLBI) observations at 22~GHz with the Korean VLBI Network (KVN) in Korea and the VLBI Exploration of Radio Astrometry (VERA) in Japan, and correlated the aquired data with the Daejeon correlator. For evaluating the performance of the new hardware correlator, we compared the correlation outputs from the Daejeon correlator for KVN observations with those from a software correlator, the Distributed FX (DiFX). We investigated the correlated flux densities and brightness distributions of extragalactic compact radio sources. The comparison of the two correlator outputs show that they are consistent with each other within $<8\%$, which is comparable with the amplitude calibration uncertainties of KVN observations at 22~GHz. We also found that the 8\% difference in flux density is caused mainly by (a) the difference in the way of fringe phase tracking between the DiFX software correlator and the Daejeon hardware correlator, and (b) an unusual pattern (a double-layer pattern) of the amplitude correlation output from the Daejeon correlator. The visibility amplitude loss by the double-layer pattern is as small as 3\%. We conclude that the new hardware correlator produces reasonable correlation outputs for continuum observations, which are consistent with the outputs from the DiFX software correlator.Comment: 13 pagee, 9 figures, 3 tables, to appear in JKAS (received February 9, 2015; accepted March 16, 2015

Experimental determination of the complete spin structure for anti-proton + proton -> anti-\Lambda + \Lambda at anti-proton beam momentum of 1.637 GeV/c

The reaction anti-proton + proton -> anti-\Lambda + \Lambda -> anti-proton + \pi^+ + proton + \pi^- has been measured with high statistics at anti-proton beam momentum of 1.637 GeV/c. The use of a transversely-polarized frozen-spin target combined with the self-analyzing property of \Lambda/anti-\Lambda decay allows access to unprecedented information on the spin structure of the interaction. The most general spin-scattering matrix can be written in terms of eleven real parameters for each bin of scattering angle, each of these parameters is determined with reasonable precision. From these results all conceivable spin-correlations are determined with inherent self-consistency. Good agreement is found with the few previously existing measurements of spin observables in anti-proton + proton -> anti-\Lambda + \Lambda near this energy. Existing theoretical models do not give good predictions for those spin-observables that had not been previously measured.Comment: To be published in Phys. Rev. C. Tables of results (i.e. Ref. 24) are available at http://www-meg.phys.cmu.edu/~bquinn/ps185_pub/results.tab 24 pages, 16 figure

Online supervised hashing

Fast nearest neighbor search is becoming more and more crucial given the advent of large-scale data in many computer vision applications. Hashing approaches provide both fast search mechanisms and compact index structures to address this critical need. In image retrieval problems where labeled training data is available, supervised hashing methods prevail over unsupervised methods. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies may be inefficient when confronted with large datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as the dataset continues to grow and new variations appear over time. To handle these issues, we propose OSH: an Online Supervised Hashing technique that is based on Error Correcting Output Codes. We consider a stochastic setting where the data arrives sequentially and our method learns and adapts its hashing functions in a discriminative manner. Our method makes no assumption about the number of possible class labels, and accommodates new classes as they are presented in the incoming data stream. In experiments with three image retrieval benchmarks, our method yields state-of-the-art retrieval performance as measured in Mean Average Precision, while also being orders-of-magnitude faster than competing batch methods for supervised hashing. Also, our method significantly outperforms recently introduced online hashing solutions.https://pdfs.semanticscholar.org/555b/de4f14630d8606e37096235da8933df228f1.pdfAccepted manuscrip

A Self-initializing Eyebrow Tracker for Binary Switch Emulation

We designed the Eyebrow-Clicker, a camera-based human computer interface system that implements a new form of binary switch. When the user raises his or her eyebrows, the binary switch is activated and a selection command is issued. The Eyebrow-Clicker thus replaces the "click" functionality of a mouse. The system initializes itself by detecting the user's eyes and eyebrows, tracks these features at frame rate, and recovers in the event of errors. The initialization uses the natural blinking of the human eye to select suitable templates for tracking. Once execution has begun, a user therefore never has to restart the program or even touch the computer. In our experiments with human-computer interaction software, the system successfully determined 93% of the time when a user raised his eyebrows.Office of Naval Research; National Science Foundation (IIS-0093367

Volumetric Calibration Refinement using masked back projection and image correlation superposition

This paper deals with a new, reconstruction based, approach of refining a volumetric calibration. The technique is based on a 2D cross-correlation between particle images on the sensor plane with a planar back projection from a tomographic reconstruction in the same sensor plane to determine potential disparities between the initial camera calibration and the measurement. Additive superposition of the correlation maps from different sets or particle images allows reducing the influence of noise and ghost particles such that the systematic errors in the calibration can be corrected. The different sections describe the theory, the principle processing steps and the convergence of the procedure. Furthermore, the concept is proven by simulating the entire process of the measurement chain, with the help of a synthetic comparison. The results show that disparities of over 9 pixels could be corrected to an average of below 0.1 pixels during the refinement steps. Finally, the technique demonstrates it´s potential to measured data, where the numbers of outliers in the raw results are reduced after the volumetric calibration refinement