OBJECT MATCHING IN DISJOINT CAMERAS USING A COLOR TRANSFER APPROACH

Object appearance models are a consequence of illumination, viewing direction, camera intrinsics, and other conditions that are specific to a particular camera. As a result, a model acquired in one view is often inappropriate for use in other viewpoints. In this work we treat this appearance model distortion between two non-overlapping cameras as one in which some unknown color transfer function warps a known appearance model from one view to another. We demonstrate how to recover this function in the case where the distortion function is approximated as general affine and object appearance is represented as a mixture of Gaussians. Appearance models are brought into correspondence by searching for a bijection function that best minimizes an entropic metric for model dissimilarity. These correspondences lead to a solution for the transfer function that brings the parameters of the models into alignment in the UV chromaticity plane. Finally, a set of these transfer functions acquired from a collection of object pairs are generalized to a single camera-pair-specific transfer function via robust fitting. We demonstrate the method in the context of a video surveillance network and show that recognition of subjects in disjoint views can be significantly improved using the new color transfer approach

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

Development of situation recognition, environment monitoring and patient condition monitoring service modules for hospital robots

An aging society and economic pressure have caused an increase in the patient-to-staff ratio leading to a reduction in healthcare quality. In order to combat the deficiencies in the delivery of patient healthcare, the European Commission in the FP6 scheme approved the financing of a research project for the development of an Intelligent Robot Swarm for Attendance, Recognition, Cleaning and Delivery (iWARD). Each iWARD robot contained a mobile, self-navigating platform and several modules attached to it to perform their specific tasks. As part of the iWARD project, the research described in this thesis is interested to develop hospital robot modules which are able to perform the tasks of surveillance and patient monitoring in a hospital environment for four scenarios: Intruder detection, Patient behavioural analysis, Patient physical condition monitoring, and Environment monitoring. Since the Intruder detection and Patient behavioural analysis scenarios require the same equipment, they can be combined into one common physical module called Situation recognition module. The other two scenarios are to be served by their separate modules: Environment monitoring module and Patient condition monitoring module. The situation recognition module uses non-intrusive machine vision-based concepts. The system includes an RGB video camera and a 3D laser sensor, which monitor the environment in order to detect an intruder, or a patient lying on the floor. The system deals with various image-processing and sensor fusion techniques. The environment monitoring module monitors several parameters of the hospital environment: temperature, humidity and smoke. The patient condition monitoring system remotely measures the following body conditions: body temperature, heart rate, respiratory rate, and others, using sensors attached to the patient’s body. The system algorithm and module software is implemented in C/C++ and uses the OpenCV image analysis and processing library and is successfully tested on Linux (Ubuntu) Platform. The outcome of this research has significant contribution to the robotics application area in the hospital environment