Pre-processing Techniques to Improve the Efficiency of Video Identification for the Pygmy Bluetongue Lizard

Copyright 2015 SCITEPRESS (Science and Technology Publications, Lda.). Published version of the paper reproduced here with permission from the publisherIn the study of the endangered Pygmy Bluetongue Lizard, non-invasive photographic identification is preferred to the current invasive methods which can be unreliable and cruel. As the lizard is an endangered species, there are restrictions on its handling. The lizard is also in constant motion and it is therefore difficult to capture a good still image for identification purposes. Hence video capture is preferred as a number of images of the lizard at various positions and qualities can be collected in just a few seconds from which the best image can be selected for identification. With a large number of individual lizards in the database, matching a video sequence of images against each database image for identification will render the process very computationally inefficient. Moreover, a large portion of those images are non-identifiable due to motion and optical blur and different body curvature to the reference database image. In this paper, we propose a number of pre-processing techniques for pre-selecting the best image out of the video image sequence for identification. Using our proposed pre-selection techniques, it has been shown that the computational efficiency can be significantly improved

Coherent Selection of Independent Trackers for Real-time Object Tracking

International audienceThis paper presents a new method for combining several independent and heterogeneous tracking algorithms for the task of online single-object tracking. The proposed algorithm runs several trackers in parallel, where each of them relies on a different set of complementary low-level features. Only one tracker is selected at a given frame, and the choice is based on a spatio-temporal coherence criterion and normalised confidence estimates. The key idea is that the individual trackers are kept completely independent, which reduces the risk of drift in situations where for example a tracker with an inaccurate or inappropriate appearance model negatively impacts the performance of the others. Moreover, the proposed approach is able to switch between different tracking methods when the scene conditions or the object appearance rapidly change. We experimentally show with a set of Online Adaboost-based trackers that this formulation of multiple trackers improves the tracking results in comparison to more classical combinations of trackers. And we further improve the overall performance and computational efficiency by introducing a selective update step in the tracking framework

Fusion of Learned Multi-Modal Representations and Dense Trajectories for Emotional Analysis in Videos

When designing a video affective content analysis algorithm, one of the most important steps is the selection of discriminative features for the effective representation of video segments. The majority of existing affective content analysis methods either use low-level audio-visual features or generate handcrafted higher level representations based on these low-level features. We propose in this work to use deep learning methods, in particular convolutional neural networks (CNNs), in order to automatically learn and extract mid-level representations from raw data. To this end, we exploit the audio and visual modality of videos by employing Mel-Frequency Cepstral Coefficients (MFCC) and color values in the HSV color space. We also incorporate dense trajectory based motion features in order to further enhance the performance of the analysis. By means of multi-class support vector machines (SVMs) and fusion mechanisms, music video clips are classified into one of four affective categories representing the four quadrants of the Valence-Arousal (VA) space. Results obtained on a subset of the DEAP dataset show (1) that higher level representations perform better than low-level features, and (2) that incorporating motion information leads to a notable performance gain, independently from the chosen representation

Rock falls impacting railway tracks. Detection analysis through an artificial intelligence camera prototype

During the last few years, several approaches have been proposed to improve early warning systems for managing geological risk due to landslides, where important infrastructures (such as railways, highways, pipelines, and aqueducts) are exposed elements. In this regard, an Artificial intelligence Camera Prototype (AiCP) for real-time monitoring has been integrated in a multisensor monitoring system devoted to rock fall detection. An abandoned limestone quarry was chosen at Acuto (central Italy) as test-site for verifying the reliability of the integratedmonitoring system. A portion of jointed rockmass, with dimensions suitable for optical monitoring, was instrumented by extensometers. One meter of railway track was used as a target for fallen blocks and a weather station was installed nearby. Main goals of the test were (i) evaluating the reliability of the AiCP and (ii) detecting rock blocks that reach the railway track by the AiCP. At this aim, several experiments were carried out by throwing rock blocks over the railway track. During these experiments, the AiCP detected the blocks and automatically transmitted an alarm signal

EyePACT: eye-based parallax correction on touch-enabled interactive displays

The parallax effect describes the displacement between the perceived and detected touch locations on a touch-enabled surface. Parallax is a key usability challenge for interactive displays, particularly for those that require thick layers of glass between the screen and the touch surface to protect them from vandalism. To address this challenge, we present EyePACT, a method that compensates for input error caused by parallax on public displays. Our method uses a display-mounted depth camera to detect the user's 3D eye position in front of the display and the detected touch location to predict the perceived touch location on the surface. We evaluate our method in two user studies in terms of parallax correction performance as well as multi-user support. Our evaluations demonstrate that EyePACT (1) significantly improves accuracy even with varying gap distances between the touch surface and the display, (2) adapts to different levels of parallax by resulting in significantly larger corrections with larger gap distances, and (3) maintains a significantly large distance between two users' fingers when interacting with the same object. These findings are promising for the development of future parallax-free interactive displays

ПОСТРОЕНИЕ КРИВОЛИНЕЙНОГО СКЕЛЕТА ТРЕХМЕРНОЙ МОДЕЛИ ПО ПЛОСКИМ ПРОЕКЦИЯМ

В данной работе предложен новый алгоритм построения криволинейного скелета для широкого класса объектов. Алгоритм использует аппроксимацию объекта его визуальной оболочкой, что дает нам возможность работать с моделью, используя только ее силуэты. Предлагается эффективный алгоритм для вычисления 3D карты расстояний для внутренних вокселей визуальной оболочки. Используя эту 3D карту расстояний, организуется обратное проецирование непрерывных скелетов плоских проекций, формирующих визуальную оболочку. Полученное облако точек является первой аппроксимацией криволинейного скелета. Затем используется набор техник фильтрации и кластеризации полученного облака с целью получения менее шумной аппроксимации. Полученная аппроксимация уже может использоваться для приложений. Далее организуется итерационный процесс для уточнения криволинейного скелета. Описываемый метод показал существенное улучшение времени вычисления по сравнению с существующими методами. Метод показал хорошие результаты построения криволинейного скелета для моделей со сложной геометрией и топологией. Получаемые криволинейные скелеты удовлетворяют большинству требований, предъявляемым к универсальным криволинейным скелета

Highly corrupted image inpainting through hypoelliptic diffusion

We present a new image inpainting algorithm, the Averaging and Hypoelliptic Evolution (AHE) algorithm, inspired by the one presented in [SIAM J. Imaging Sci., vol. 7, no. 2, pp. 669--695, 2014] and based upon a semi-discrete variation of the Citti-Petitot-Sarti model of the primary visual cortex V1. The AHE algorithm is based on a suitable combination of sub-Riemannian hypoelliptic diffusion and ad-hoc local averaging techniques. In particular, we focus on reconstructing highly corrupted images (i.e. where more than the 80% of the image is missing), for which we obtain reconstructions comparable with the state-of-the-art.Comment: 15 pages, 10 figure