A Trajectory Evaluator by Sub-tracks for Detecting VOT-based Anomalous Trajectory

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.

A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-ResnetV2 Models

Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.

Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams

The neural basis of object recognition and semantic knowledge has been extensively studied but the high dimensionality of object space makes it challenging to develop overarching theories on how the brain organises object knowledge. To help understand how the brain allows us to recognise, categorise, and represent objects and object categories, there is a growing interest in using large-scale image databases for neuroimaging experiments. In the current paper, we present THINGS-EEG, a dataset containing human electroencephalography responses from 50 subjects to 1,854 object concepts and 22,248 images in the THINGS stimulus set, a manually curated and high-quality image database that was specifically designed for studying human vision. The THINGS-EEG dataset provides neuroimaging recordings to a systematic collection of objects and concepts and can therefore support a wide array of research to understand visual object processing in the human brain.

CAT: Centerness-Aware Anchor-Free Tracker

Accurate and robust scale estimation in visual object tracking is a challenging task. To obtain a scale estimation of the target object, most methods rely either on a multi-scale searching scheme or on refining a set of predefined anchor boxes. These methods require heuristically selected parameters, such as scale factors of the multi-scale searching scheme, or sizes and aspect ratios of the predefined candidate anchor boxes. On the contrary, a centerness-aware anchor-free tracker (CAT) is designed in this work. First, the location and scale of the target object are predicted in an anchor-free fashion by decomposing tracking into parallel classification and regression problems. The proposed anchor-free design obviates the need for hyperparameters related to the anchor boxes, making CAT more generic and flexible. Second, the proposed centerness-aware classification branch can identify the foreground from the background while predicting the normalized distance from the location within the foreground to the target center, i.e., the centerness. The proposed centerness-aware classification branch improves the tracking accuracy and robustness significantly by suppressing low-quality state estimates. The experiments show that our centerness-aware anchor-free tracker, with its appealing features, achieves salient performance in a wide variety of tracking scenarios.

GLE-Net: A Global and Local Ensemble Network for Aerial Object Detection

Recent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning field, we introduce a novel integration strategy to combine the inference results of two different methods without non-maximum suppression. In this paper, a global and local ensemble network (GLE-Net) was proposed to increase the quality of predictions by considering the global weights for different models and adjusting the local weights for bounding boxes. Specifically, the global module assigns different weights to models. In the local module, we group the bounding boxes that corresponding to the same object as a cluster. Each cluster generates a final predict box and assigns the highest score in the cluster as the score of the final predict box. Experiments on benchmarks VisDrone2019 show promising performance of GLE-Net compared with the baseline network.

Efficient Online Object Tracking Scheme for Challenging Scenarios

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.

DEVELOPMENT OF AN AUTOMATED SYSTEM FOR DIAGNOSING EYE DISEASES BASED ON PRODUCTION RULES

В статье рассматриваются вопросы диагностики заболеваний органов зрения и его придаточного аппарата. Медико-социальное значение болезней органов зрения и его придаточного аппарата в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Так как зрение является для человека важнейшим из всех органов чувств, без которого невозможна полноценная жизнь, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики заболеваний глаз является автоматизация обработки диагностических данных с использованием современных технологий, а именно компьютерной системы поддержки принятия решений. Данная статья посвящена разработке автоматизированной системы диагностики заболеваний глаза на основе продукционных правил. Следует отметить, что процесс медицинского офтальмологического исследования занимает значительное время на различного рода лабораторные анализы, инструментальную диагностику, опрос больного или физического исследования. Автоматизированная компьютерная система диагностики глазных заболеваний предназначена для автоматического установления по характерным признакам таких диагнозов как острый конъюнктивит, острый ирит, острый приступ глаукомы и катаракта. Разработанная программа, реализованная в системе визуального объектно-ориентированного программирования С++, представляется пользователям как консультант для автоматизации работы, что позволит повысить эффективность процесса диагностики заболеваний органов зрения и его придаточного аппарата The article deals with the diagnosis of diseases of the organs of vision and its accessory apparatus. The medical and social significance of diseases of the organs of vision and its accessory apparatus in modern conditions is great and is determined, first of all, by their extremely high frequency among various contingents of the population. Since vision is the most important of all sense organs for a person, without which a full life is impossible, it is necessary to identify various pathologies in time and apply immediate treatment measures. One of the means to increase the effectiveness of the diagnosis of eye diseases is the automation of diagnostic data processing using modern technologies, namely a computer decision support system. This article is devoted to the development of an automated system for diagnosing eye diseases based on production rules. It should be noted that the process of medical ophthalmological examination takes considerable time for various kinds of laboratory tests, instrumental diagnostics, patient interview or physical examination. The automated computer system for the diagnosis of eye diseases is designed to automatically establish the characteristic signs of such diagnoses as acute conjunctivitis, acute iritis, acute attack of glaucoma and cataract. The developed program, implemented in the C++ visual object-oriented programming system, is presented to users as a consultant for automating work, which will increase the efficiency of the process of diagnosing diseases of the visual organs and its accessory apparatus