Information theoretic sensor management for multi-target tracking with a single pan-tilt-zoom camera

Automatic multiple target tracking with pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the lit-erature, most of them proposing simplistic scenarios. In this paper, we present a PTZ camera management framework which lies on information theoretic principles: at each time step, the next camera pose (pan, tilt, focal length) is chosen, according to a policy which ensures maximum information gain. The formulation takes into account occlusions, phys-ical extension of targets, realistic pedestrian detectors and the mechanical constraints of the camera. Convincing com-parative results on synthetic data, realistic simulations and the implementation on a real video surveillance camera val-idate the effectiveness of the proposed method. 1

Non-myopic information theoretic sensor management of a single pan\u2013tilt\u2013zoom camera for multiple object detection and tracking

Detailed derivation of an information theoretic framework for real PTZ management.Introduction and implementation of a non-myopic strategy.Large experimental validation, with synthetic and realistic datasets.Working demonstration of myopic strategy on an off-the-shelf PTZ camera. Automatic multiple object tracking with a single pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the literature, most of them proposing simplistic scenarios. In this paper, we present a novel PTZ camera management framework in which at each time step, the next camera pose (pan, tilt, focal length) is chosen to support multiple object tracking. The policy can be myopic or non-myopic, where the former analyzes exclusively the current frame for deciding the next camera pose, while the latter takes into account plausible future target displacements and camera poses, through a multiple look-ahead optimization. In both cases, occlusions, a variable number of subjects and genuine pedestrian detectors are taken into account, for the first time in the literature. Convincing comparative results on synthetic data, realistic simulations and real trials validate our proposal, showing that non-myopic strategies are particularly suited for a PTZ camera management

Standalone CADx

Background and Rationale: Real-time in vivo Optical Characterization (OC) of colorectal polyps during colonoscopy has recently gained traction as means to guide ‘resect or discard’ strategies, potentially leading to decreased complications, physician burden, and medical costs. However, this characterization task is challenging and suffers from significant inter- and intra-observer variability, often resulting in community endoscopists performances below accepted thresholds. Several learning-based models have been recently proposed to aid this characterization task. Still, none of them can work on real-time regular colonoscopy videos, as they have either been developed for the characterization of still images or they require human intervention to cope with variations of image quality and polyp appearance. In this study, we intend to assess the performances of a deep learning-based module for real-time characterization of colorectal polyp's histology that can be coupled with any automated polyp detection model in colonoscopy videos (GI Genius CADx). The module consists of a convolutional neural network model which classifies each detected polyp in a single video frame as adenomatous or non-adenomatous polyp and tracks it across all the video frames. This algorithm produces stable, spatio-temporally weighted decisions that are displayed real-time on each frame. The AI model can also abstain from predicting the polyp histology if insufficient confidence was accumulated. The GI Genius CADx was developed to help endoscopists in their clinical practices for polyp characterization. In order to assess GI Genius CADx performances in terms of prediction accuracy, the output of GI Genius CADx (adenoma/non-adenoma/no-prediction) will be evaluated against the histopathology reference standard (ground truth). The performances of GI Genius CADx will be compared against the prediction of the same lesions performed by a pool of endoscopists reviewing the video recording of the procedures blind to the histology results. Objective: To prospectively evaluate if GI Genius CADx accuracy in the automated OC of colorectal polyps in white light is non-inferior to the accuracy of expert endoscopists performing OC (supported by virtual chromoendoscopy), having histopathology as a reference standard. Furthermore, to evaluate if GI Genius CADx accuracy in the automated OC of colorectal polyps in white light is superior to the accuracy of non-expert endoscopists performing OC (supported by virtual chromoendoscopy), having histopathology as a reference standard. Furthermore, to evaluate if GI Genius CADx accuracy in the automated OC of colorectal polyps in white light is non-inferior to GI Genius CADx accuracy in virtual chromoendoscopy, having histopathology as a reference standard

An Experimental Framework for Evaluating PTZ Tracking Algorithms

PTZ (Pan-Tilt-Zoom) cameras are powerful devices in video surveillance applications, because they offer both wide area coverage and highly detailed images in a single device. Tracking with a PTZ camera is a closed loop procedure that involves computer vision algorithms and control strategies, both crucial in developing an effective working system. In this work, we propose a novel experimental framework that allows to evaluate image tracking algorithms in controlled and repeatable scenarios, combining the PTZ camera with a calibrated projector screen on which we can play different tracking situations. We applied such setup to compare two different tracking algorithms, a kernel-based (mean-shift) tracking and a particle filter, opportunely tuned to fit with a PTZ camera. As shown in the experiments, our system allows to finely investigate pros and cons of each algorithm

PERSON RE-IDENTIFICATION WITH A PTZ CAMERA: AN INTRODUCTORY STUDY

RGB-Did iLIDS-MA We present an introductory study that paves the way for a new kind of person re-identification, by exploiting a single Pan-Tilt-Zoom (PTZ) camera. PTZ devices allow to zoom on body regions, acquiring discriminative visual patterns that enrich the appearance description of an individual. This intuition has been translated into a statistical direct reidentification scheme, which collects two images for each probe subject: the first image captures the probe individual, focusing on the whole body; the second can be a zoomed body part (head, torso or legs) or another whole body image, and is the outcome of an action-selection mechanism, driven by feature selection principles. The validation of this technique is also explored: in order to allow repeatability, two novel multi-resolution benchmarks have been created. On these data, we demonstrate that our approach selects effective actions, by focusing on body portions which discriminate each subject. Moreover, we show that the proposed compound of two images overwhelms standard multi-shot descriptions, composed by many more pictures. Index Terms — Person Re-identification, Pan-Tilt-Zoom camera 1

Learning How to Teach from "Videolectures": Automatic Prediction of Lecture Ratings Based on Teacher's Nonverbal Behavior

Large repositories of presentation recordings (e.g.,\u201cVideolectures\u201d and \u201cAcademic Earth\u201d) often provide their userswith rating facilities. The rating of a presentation certainlydepends on the content, but the way the content is delivered islikely to play a role as well. This paper focuses on the latter aspectand shows that nonverbal behavior (in particular arms movementand prosody) allows one to predict whether a presentation is ratedas low or high in terms of quality. The experiments have beenperformed over 100 presentations collected from \u201cVideolectures\u201dand the accuracy is up to 66% depending on the techniquesadopted. In other words, nonverbal communication actuallyinfluences the ratings assigned to a presentation

Biomimetic Locomotion on Water of a Porous Natural Polymeric Composite

Observation of the natural world can provide invaluable information on the mechanisms that semi-aquatic living organisms or bacteria use for their self-propulsion. Microvelia, for example, uses wax excreted from its legs to move on water in order to escape from predators or reach the bank of the river. Mimicking such mechanism, few self-propelled materials on water, as camphor, have been previously developed, but weak points like slow locomotion, short movement duration, or shape restrictions still need to be improved. This study presents a totally green self-assembled porous system, formed by the combination of a natural polymer with an essential oil that spontaneously moves on water for hours upon expulsion of the oil, with high velocities reaching 15 cm s(-1). The structural characteristics of the natural polymeric composite are carefully analyzed and associated to its spontaneous movement. Surface tension experiments are also presented that connect the essential oil release with the locomotion of the porous composite films. This research work opens novel routes toward bioinspired natural materials that can be used for mimicking and studying the motion of bioorganisms and microorganisms, and for applications such as energy harvesting, aquatic pollution monitoring, drug delivery, to name few

Evaluation of efficacy of preservatives associated with Achillea millefolium L. extract against Bacillus subtilis Avaliação da eficácia de conservantes associados a extrato de Achillea millefolium L. contra Bacillus subtilis

The antimicrobial efficacy of three preservatives used in cosmetic formulations was evaluated. Phenova® and imidazolidinyl urea inhibited the growth of Bacillus subtilis when added to leaf extract of Achillea millefolium L., whereas 0.2% Nipagin®/ Nipasol® in propylene glycol did not.<br>A eficácia antimicrobiana de conservantes empregados em formulações cosméticas foi avaliada usando Phenova® e Imidazolinidil uréia que inibiram o crescimento de Bacillus subtilis no extrato de Achillea millefolium L. e Nipagin®/ Nipasol® 0,2% em propilenoglicol não apresentaram efeito microbicida