A New Multiple Hypothesis Tracker Using Validation Gate with Motion Direction Constraint.

In multi-target tracking scenarios with dense and heterogeneous clutter, there is a substantial increase in the false measurements that originated from the clutter within the validation gate, and consequently, the number of measurement-to-track association hypothesis grows rapidly in traditional multiple hypothesis tracker (MHT), leading to a sharp decrease in data association accuracy and tracking performance. A new multiple hypothesis tracker using validation gate with motion direction constraint (MHT-MDC) is proposed to solve these problems. In the MHT-MDC, a motion direction constraint (MDC) gate is designed by considering the prior target maneuvering information, which effectively reduces the volume of validation gate and, thus, diminishes the number of false measurements in the gate when the innovation covariance is large. Subsequently, the clutter density in the MDC gate is adaptively estimated by the conditional mean estimator of clutter density (CMECD), based on which the score functions in the MDC gate can be calculated. The MHT-MDC is compared with the MHT algorithm in simulations, and the experimental results demonstrate its superior tracking performance for weakly maneuvering targets in high clutter density scenarios

Occlusion reasoning for multiple object visual tracking

Thesis (Ph.D.)--Boston UniversityOcclusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications. In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions. Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "nonmaximum suppression" of hypotheses by modeling the likelihood of the entire image. The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches

Track-oriented multiple hypothesis tracking based on Tabu search and Gibbs sampling

In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance

Space debris cataloging of GEO objects by using Meta-Heuristic methods

Currently several thousands of objects are being tracked in the Medium Earth Orbit (MEO) and Geosynchronous Earth Orbit (GEO) regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem becomes an NP-hard combinatorial optimization problem as soon as its dimension S becomes S ≥ 3. In regions with a high density of objects the MTT problem will have to have this dimension in order to avoid ambiguous solutions. With the advent of improved sensors and a eightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to identify an algorithm capable of addressing the problem of space debris cataloging in the MEO and GEO regions, in particular for highly dense regions, without possessing a restrictive computational complexity. In an attempt to find an approximate solution of sufficient quality several Population Based Meta Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. In addition to this, a novel way of orbit determination is presented which is based on an existing S = 2 tracklet association method. These first results show promise as one of the tested algorithms (the Elitist Genetic Algorithm (EGA)) consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. Furthermore, the results suggest that the algorithm has a polynomial time complexity when finding approximate solutions. The algorithm is also applied to real observations, where it also performs as desired

Tunable Security for Deployable Data Outsourcing

Security mechanisms like encryption negatively affect other software quality characteristics like efficiency. To cope with such trade-offs, it is preferable to build approaches that allow to tune the trade-offs after the implementation and design phase. This book introduces a methodology that can be used to build such tunable approaches. The book shows how the proposed methodology can be applied in the domains of database outsourcing, identity management, and credential management

Tracking-Reconstruction or Reconstruction-Tracking?

We developed two methods for tracking multiple objects using several camera views. The methods use the Multiple Hypothesis Tracking (MHT) framework to solve both the across-view data association problem (i.e., finding object correspondences across several views) and the across-time data association problem (i.e., the assignment of current object measurements to previously established object tracks). The "tracking-reconstruction method" establishes two-dimensional (2D) objects tracks for each view and then reconstructs their three-dimensional (3D) motion trajectories. The "reconstruction-tracking method" assembles 2D object measurements from all views, reconstructs 3D object positions, and then matches these 3D positions to previously established 3D object tracks to compute 3D motion trajectories. For both methods, we propose techniques for pruning the number of association hypotheses and for gathering track fragments. We tested and compared the performance of our methods on thermal infrared video of bats using several performance measures. Our analysis of video sequences with different levels of densities of flying bats reveals that the reconstruction-tracking method produces fewer track fragments than the tracking-reconstruction method but creates more false positive 3D tracks

Mediating Human-Robot Collaboration through Mixed Reality Cues

abstract: This work presents a communication paradigm, using a context-aware mixed reality approach, for instructing human workers when collaborating with robots. The main objective of this approach is to utilize the physical work environment as a canvas to communicate task-related instructions and robot intentions in the form of visual cues. A vision-based object tracking algorithm is used to precisely determine the pose and state of physical objects in and around the workspace. A projection mapping technique is used to overlay visual cues on tracked objects and the workspace. Simultaneous tracking and projection onto objects enables the system to provide just-in-time instructions for carrying out a procedural task. Additionally, the system can also inform and warn humans about the intentions of the robot and safety of the workspace. It was hypothesized that using this system for executing a human-robot collaborative task will improve the overall performance of the team and provide a positive experience to the human partner. To test this hypothesis, an experiment involving human subjects was conducted and the performance (both objective and subjective) of the presented system was compared with a conventional method based on printed instructions. It was found that projecting visual cues enabled human subjects to collaborate more effectively with the robot and resulted in higher efficiency in completing the task.Dissertation/ThesisMasters Thesis Electrical Engineering 201

BIM-Vision-Based Indoor Localization Prototype

[[abstract]]Purpose In order to provide an economical indoor location detective technique, this study is uses photo images as the indoor spatial identification tags associated with the spatial information of the existing building information model (BIM), so that users can identify their locations via the camera on mobile device based on the real images. Method Unlike the wireless and radio frequency identification based indoor positioning techniques, this study applied the image recognition technique to indoor location detection. Three functional modules, namely, (i) BIM object location collector, (ii) spatial image management module, and (iii) vision-based location recognition module are developed. BIM object location collec- tor takes the responsibility to automatically collect the location data from the IFC (Industry Foundation Classes) dataset of the existing BIM. Firstly, the spatial image management module provides an interface to clients for collecting spatial photos in buildings, and bind them with the location data transferred from the original buildinginformation model. Second- ly, the recognizable features of the spatial photos can be analyzed by the visionbased location recognition module which is developed based on the Dâfusion studio. According to the analyzed image features, the vision-based location recogni- tion module can then recognize the frames of the visions captured by the mobile device camera. Once the frame is rec- ognized, the corresponding spatial data can be retrieved. Results & Discussion Based on the designed architecture, a BIM-vision-based indoor localization prototype was developed as an android platform application running on the mobile device such as smart phones and tablets. Technique feasibility is continuously tested in the current phase. According to the basic test results, the prototype can identify the indoor locations of decorated spaces; however, once the indoor spaces lack of recognizable features, such as the empty spaces with blank and monotony walls, the recognition function failed. To overcome this defect, the Quick Response (QR) code, the trademark for a type of two-dimensional code, is used as a substitution of the photos for this prototype. Besides, since the location data is transferred from the existing building information model, data consistency can be ensured. In the future, the economic feasibility of this prototype will be analyzed to evaluate the cost-benefit ratio.[[conferencetype]]國際[[conferencedate]]20120627~20120629[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Eindhoven, Netherland