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http://hdl.handle.net/11375/22126
Title: | Tracking Pedestrians with Known/Unknown Interactions and Influences |
Authors: | Krishnan, Krishanth |
Advisor: | Kirubarajan, Thia |
Department: | Electrical and Computer Engineering |
Keywords: | pedestrian tracking;multiple model social forces filter;social force Kalman filter;parameter estimation with tracking;dependent motion tracking;social force PhD filter |
Publication Date: | Nov-2017 |
Abstract: | This thesis addresses the problem of tracking multiple ground targets whose motion is dependent on one another. Multiple approaches which integrate the social force based motion model into different filtering algorithms are proposed. The social force concept has previously been used to model pedestrian motion where the interactions among pedestrians are described using social forces. First, the social force based motion model integrated into the Probability Hypothesis Density (PHD) framework is proposed. Two different implementations, namely, the Sequential Monte Carlo (SMC) technique and the Gaussian Mixture (GM) technique, are derived to implement the proposed Social Force PHD (SF-PHD) filter in ground target tracking scenarios. Next, a social-force-based motion model integrated into the stacked Kalman filter (stacked SF-KF) is developed and its multiple model (stacked IMM-SF-KF) variant is derived. Then, the assumption used in the proposed algorithms, that the actual values of the social force parameters are known, is not valid at all times and the assumption is relaxed. Hence, simultaneous parameter estimation techniques for the social force parameters during the tracking are proposed. Three approaches based on the state augmentation method, the Expectation Maximization (EM) method and the maximum likelihood method are derived. The maximum likelihood method can be implemented offline or online, depending on the requirement. The traditional Posterior Cramer Rao Lower Bound (PCRLB), which is the inverse of the Fisher information matrix, gives a bound on the optimal achievable accuracy of the estimated state of a target with independent motion. Subsequently, a modified performance measure based on the PCRLB for targets whose motion is dependent on each other is derived to validate the performance of the proposed algorithms. Finally, the PCRLB that accounts for unknown interactions is derived to validate the proposed simultaneous parameter estimation techniques. Simulated and real data are used to show the performance of the proposed algorithms and simultaneous parameter estimation techniques compared to the algorithms in the literature. |
URI: | http://hdl.handle.net/11375/22126 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Krishanth_Krishnan_2017May_PhD.pdf | 792.89 kB | Adobe PDF | View/Open |
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