A Fast and Accurate Approach to Multiple-Vehicle Localization and Tracking from Monocular Aerial Images

In this work we present a novel end-to-end solution for tracking objects (i.e., vessels), using video streams from aerial drones, in dynamic maritime environments. Our method relies on deep features, which are learned using realistic simulation data, for robust object detection, segmentation and tracking. Furthermore, we propose the use of rotated bounding-box representations, which are computed by taking advantage of pixel-level object segmentation, for improved tracking accuracy, by reducing erroneous data associations during tracking, when combined with the appearance-based features. A thorough set of experiments and results obtained in a realistic shipyard simulation environment, demonstrate that our method can accurately, and fast detect and track dynamic objects seen from a top-view.

The Concept of Implementation of Multifrequency RFID System Industrial Involvement in Laboratory Conditions

People have been dealing with the correct identification of objects for a long time. In industry, we cannot avoid this area, whether it is to identify people, semi-finished products or final products. Therefore, this article deals with the design of a multifrequency RFID system for industry 4.0. The idea of the article is to implement one type of identification technology for tracking objects using the radio frequency spectrum at different wavelengths. We have based our design on the built industrial-assembly line in the SmartTechLab laboratory, where we have implemented LF, HF and UHF systems connected by an industrial PLC into a complex system. In this article, we gradually focus on the selection of RFID systems, their cooperation and the design of connection to one portable box. Using an RFID box, we can monitor different types of objects and verify RFID reading using a single reading device or by creating portal RFID gateways. The implemented system consists of four middleware and four independent antennas that can cooperate. For proper operation, there is necessary implement not only hardware but also necessary software. The system can identify RFID tags in the range of 1 cm to several meters. Also, the advantage of the design is that it identifies all types of tags (industry, label, ceramic, laundry, paper). One of the main benefits of the design is modularity, mobility and the creation of a robust design that can be used for measurements in companies and also for educating students in laboratory conditions. The whole system is designed to meet the requirements of Industry 4.0 and improve the competitiveness of businesses.

Experimental Reflection Evaluation for Attitude Monitoring of Space Orbiting Systems with NRL Arch Method

The increasing number of satellites orbiting around Earth has led to an uncontrolled increase in objects within the orbital environment. Since the beginning of the space age on 4 October 1957 (launch of Sputnik I), there have been more than 4900 space launches, leading to over 18,000 satellites and ground-trackable objects currently orbiting the Earth. For each satellite launched, several other objects are also sent into orbit, including rocket upper stages, instrument covers, and so on. Having a reliable system for tracking objects and satellites and monitoring their attitude is at present a mandatory challenge in order to prevent dangerous collisions and an increase in space debris. In this paper, the evaluation of the reflection coefficient of different shaped objects has been carried out by means of the bi-static reflection method, also known as NRL arch measurement, in order to evaluate their visibility and attitude in a wide range of frequencies (12–18 GHz). The test campaign aims to correlate the experimental measures with the hypothetical reflection properties of orbiting systems.

Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.

Deep Compressive Offloading

Future mobile and embedded systems will be smarter and more user-friendly. They will perceive the physical environment, understand human context, and interact with end-users in a human-like fashion. Daily objects will be capable of leveraging sensor data to perform complex estimation and recognition tasks, such as recognizing visual inputs, understanding voice commands, tracking objects, and interpreting human actions. This raises important research questions on how to endow low-end embedded and mobile devices with the appearance of intelligence despite their resource limitations.

Indoor Distance Measurement System COPS (COVID-19 Prevention System)

With the rapid spread of coronavirus disease 2019 (COVID-19), measures are needed to detect social distancing and prevent further infection. In this paper, we propose a system that detects social distancing in indoor environments and identifies the movement path and contact objects according to the presence or absence of an infected person. This system detects objects through frames of video data collected from a closed-circuit television using You Only Look Once (v. 4) and assigns and tracks object IDs using DeepSORT, a multiple object tracking algorithm. Next, the coordinates of the detected object are transformed by image warping the area designated by the top angle composition in the original frame. The converted coordinates are matched with the actual map to measure the distance between objects and detect the social distance. If an infected person is present, the object that violates the movement path and social distancing of the infected person is detected using the ID assigned to each object. The proposed system can be used to prevent the rapid spread of infection by detecting social distancing and detecting and tracking objects according to the presence of infected persons.

Object detection and tracking in video sequences: formalization, metrics and results

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

METHODS OF IMAGE RECOGNITION IN A VIDEO STREAM

The article examines the problem of automatic object recognition using a video stream as a digital image. Algorithms for recognizing and tracking objects in the video stream are considered, methods used in video processing are analyzed, and the use of machine learning tools in working with video is described.The main approaches to solving the problem of recognizing moving objects in a video stream are investigated: the detection-based approach and the tracking-based approach. Arguments are made in favor of the tracking-based approach, and, in addition, modern methods of tracking objects in the video stream are considered. In particular, the algorhythms: Online Boosting Tracker - one of the first object tracking algorithms with high tracking accuracy, MIL Tracker (Multiple Instance Learning Tracker), which is a development of the idea of learning with a teacher and the Online Boosting algorithm and the KCF Tracker algorithm (Kernelized Correlation Filters Tracker) - a method that uses the mathematical properties of overlapping areas of positive examples.As a result, the advantages and disadvantages of the considered methods and algorithms for recognizing and tracking objects for various applications are highlighted.

Optimal Metric Evaluation-Based Multicue Inverse Sparse Appearance Model for Object Tracking

In order to obtain the discriminative compact appearance model for tracking objects effectively, this paper proposes a new structural tracking strategy that includes multicue inverse sparse appearance model and optimal metric evaluation between online robust templates and a limited number of particle samples in the looping process. Multicue inverse sparse appearance model globally improves the efficient selection of informative particle samples that can avoid the cumbersome coding and decoding cost for the trivial random particle samples. Only the most potential crucial cases are involved in each tracking loop. This refrains from unreasonable, rough numerical reduction of particle samples and also keeps the unbiasedness and dynamic stochasticness of the sampling process. Meanwhile, low-rank self-representatives for positive and negative samples facilitate the formulation of a suitable code book that arranges the useful sparse coefficients for feature bags and facilitates optimal metric evaluation for online training. It also alleviates the accuracy degradation of tracking occluded objects and improves the robustness of the tracker. Both of them preserve the discriminative compactness of target which speeds up particle filtering localization to separate the target object from distractors. Moreover, the proposed method exploits online appearance representations to learn the sharing compact information that avoids massive calculation burdens for massive visual data.