Computer Vision-based Tracking Research Articles

Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach

This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework makes use of multiple sensors, data pipelines, and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high-quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework’s validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort, and SiamMOT, are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.

Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet

Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet

Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects

This paper proposes a pseudo-haptic interface that depicts the virtual weights of dumbbells in a virtual gymnasium. When a user performs a dumbbell biceps curl, he/she fixes the elbow joint as a standard joint and lifts the dumbbell, with its movement trajectory represented as a circular arc. The trajectories and velocity of dumbbell bicep curls differ depending on human physiological characteristics. Therefore, the proposed system provides an adaptable exercise area and force visualization of virtual dumbbells using a velocity-based pseudo-haptic interface and computer vision-based tracking method. The system recognizes the position and rotation of joints related to a dumbbell biceps curl with the implementation of density-based spatial clustering of applications with noise (a clustering algorithm) and resizes the radius and angle of an integrated force circular gauge. Furthermore, when a user lifts a dumbbell, the system recognizes, using linear regression, the current position and lifting force of the virtual dumbbell and visualizes the current lifting force with a guided movement trajectory to match the lifting force. Experimental results show that the proposed pseudo-haptic interface increased weight perception and usability by up to 30% compared to conventional methods (p < 0.05).

Deep Learning Era for Computer Vision-Based Eye Gaze Tracking: An Intensive Model

The active theme for exploration is currently the potential for visual gaze tracking and visual human–machine interactions in contemporary user devices. Eye views were used as an input tool to extract human behavioral knowledge and to achieve immersive user interactions in augmented and amplified reality systems. However, implementations of gaze on consumer devices operating under controlled circumstances face tough precision and reliability challenges. For evolving automotive electronic systems such as driver surveillance systems and new touch screens, accurate and successful gaze estimates are significant. Such systems have to work efficiently and at minimum cost in demanding unregulated, low-power conditions. Deep learning is omnipresent for most computer vision tasks, but it is still common to estimate the eye gaze. The realistic account of all challenges and opportunities of eye gaze tracking requires a complete study of available deep learning methods. In this chapter, huge potential efforts are made to study the background of eye gaze tracking with the detail of how deep learning made a contribution to computer vision-based tracking. In the end, this chapter also highlights a generic system model and comparison of various algorithms for deep learning-driven eye gaze direction diagnosis.

AUGMENTED REALITY NAVIGATION FRAMEWORK FOR TOTAL HIP ARTHROPLASTY SURGERY

In a total hip arthroplasty surgery, correctly implanting the artificial acetabulum and the femoral head is essential for a successful treatment. An augmented reality (AR) navigation framework is proposed in this paper to provide accurate surgical guidance in a total hip arthroplasty procedure. The AR framework consists of three parts: (1) preoperative surgical planning to generate virtual information for AR; (2) computer vision-based tracking for the real-time localization of both acetabular cup positioner and bony landmarks during surgery; (3) registration of a virtual object onto a real-world operative field to properly overlay the preoperative surgical planning data onto a three-dimensional (3D)-printed pelvis model. The cost-effective framework is designed with our clinical partner based on real surgical conditions. The bony landmarks are automatically detected and used for the registration between virtual and real objects. The AR performance is evaluated with a pelvis model, and it presents mean errors of 2.2[Formula: see text]mm and 0.8∘ in position and orientation, respectively, between real and virtual spaces. These small errors are within the tolerance of positive surgical outcomes.

A survey on Deep Learning Based Eye Gaze Estimation Methods

In recent years, deep-learning systems have made great progress, particularly in the disciplines of computer vision and pattern recognition. Deep-learning technology can be used to enable inference models to do real-time object detection and recognition. Using deep-learning-based designs, eye tracking systems could determine the position of eyes or pupils, regardless of whether visible-light or near-infrared image sensors were utilized. For growing electronic vehicle systems, such as driver monitoring systems and new touch screens, accurate and successful eye gaze estimates are critical. In demanding, unregulated, low-power situations, such systems must operate efficiently and at a reasonable cost. A thorough examination of the different deep learning approaches is required to take into consideration all of the limitations and opportunities of eye gaze tracking. The goal of this research is to learn more about the history of eye gaze tracking, as well as how deep learning contributed to computer vision-based tracking. Finally, this research presents a generalized system model for deep learning-driven eye gaze direction diagnostics, as well as a comparison of several approaches.

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning.

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation -score of 0.93. The algorithm measured an average classification and tracking -score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.