We present a new formulation of the constellation model with correlation filters that treats the geometric and visual constraints within a single convex cost function and derive a highly efficient optimization for maximum a posteriori inference of a fully connected constellation. We propose a tracker that models the object at two levels of detail. The coarse level approximately localizes the object, while the mid-level representation carries out fine localization. The model is capable of adapting to the target aspect change and partial occlusion, which is often the case in tracking in marine environment. The resulting tracker is rigorously analyzed on a highly challenging OTB, VOT2014, and VOT2015 benchmarks, exhibits a state-of-the-art performance and runs in real-time.
COBISS.SI-ID: 1537625283
Room categorization, i.e., recognizing the functionality of a never before seen room, is a crucial capability for a household mobile robot. We present a new approach for room categorization that is based on 2D laser range data. The method is based on a novel spatial model consisting of mid-level parts that are built on top of a low-level partbased representation. The approach is then fused with a vision-based method for room categorization, which is also based on a spatial model consisting of mid-level visual-parts. In addition, we propose a new discriminative dictionary learning technique that is applied for part-dictionary selection in both laser-based and vision-based modalities. Finally, we present a comparative analysis between laser-based, vision-based, and laser-vision-fusion-based approaches in a uniform part-based framework that is evaluated on a large dataset with several categories of rooms from the domestic environments.
COBISS.SI-ID: 1537424323
This paper presents a novel robust method for single target tracking in RGB-D images, and also contributes a substantial new benchmark dataset for evaluating RGB-D trackers. Much of the previous RGB-D literature relies on color information for tracking, while exploiting depth information only for occlusion reasoning. In contrast, we propose an adaptive range-invariant target depth model, and show how both depth and color information can be fully and adaptively fused during the search for the target in each new RGB-D image. We introduce a new, hierarchical, two-layered target model (comprising local and global models) which uses spatio-temporal consistency constraints to achieve stable and robust on-the-fly target relearning. We show how combining target information with contextual information enables the target's depth constraint to be relaxed. Our tracker performs favorably on two state-of-the-art methodologies VOT and OTB.
COBISS.SI-ID: 1537680579
Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This work presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, this work also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community.
COBISS.SI-ID: 1537395395
In this paper we address the problem of developing on-line visual tracking algorithms. We present a specialized communication protocol that serves as a bridge between a tracker implementation and utilizing application. It decouples development of algorithms and application, encouraging reusability. The primary use case is algorithm evaluation where the protocol facilitates complex evaluation scenarios specific for various robotic applications like robotic boats. We present a reference implementation of the protocol that makes it easy to use in several popular programming languages and discuss where the protocol is already used and some usage scenarios that we envision for the future.
COBISS.SI-ID: 1537470147