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The tracking of moving object by implementing robot manipulator is one of the challenging task for many applications such as manufacturing, agriculture, logistics, healthcare, space, military, entertainment, etc. In the deployment of robotic manipulators with real-time object tracking for aforementioned important applications, the proper sensor surveillance and ensuring stability are major challenges. The purpose of this study is to design a precise and responsive object-tracking system by eliminating the complexities related to tedious mechanisms, rigidity, requirement of multiple sensors, etc. which are commonly associated with traditional systems. The robotic arms can be effectively designed to track moving objects autonomously with vision-based control. In comparison with different classical and traditional servoing approaches, the image-based visual servoing (IBVS) is more advantageous in vision-based control. The present article describes a new approach for IBVS-based tracking control of 2-degree-of-freedom (DOF) robotic arm by including object identification and trajectory tracking based crucial components. To solve the issues associated with IBVS, an accurate deep learning-based object detection framework is employed. The presented framework is utilized to detect and locate the objects in real-time. Further, an effective vision-based control technique is designed to control the 2-DOF robotic arm with the help of real-time response of object detection system. The validation of proposed control strategy is done by performing a simulation and experimental investigations with CoppeliaSim robot simulator and 2-DOF robotic arm, respectively. The findings reveal that the proposed deep learning controller for the vision-based 2-DOF robotic arm achieves good levels of accuracy and response time while performing visual servoing tasks. Furthermore, thorough discussion on possibility of using data-driven learning technique has been explored to improve the robustness and adaptability of the presented control scheme.

Aims Assessment of left atrial (LA) function and the left atrioventricular coupling index (LACI) have recently been increasingly recognized as important indices for cardiovascular diseases associated with the presence of prediabetes and diabetes. We aimed to evaluate LA function and the LACI in patients with prediabetes and diabetes via cardiac magnetic resonance (CMR). Methods In this retrospective study, we included 35 patients with prediabetes, 32 patients with diabetes, and 84 healthy control participants. The LACI and LA total, passive, and active emptying fractions (LATEmF, LAPEmF, and LAAEmF, respectively) were calculated. The LA reservoir, conduit, and booster pump strains (ε s , ε e , and ε a ), and peak positive, peak early negative, and peak late negative strain rates (SRs, SRe, and SRa) were obtained via CMR-feature tracking (CMR-FT). For the statistical analyses, one-way analysis of variance, the Kruskal–Wallis test, and linear regression were conducted, and Pearson’s and interclass correlation coefficients were calculated. Results Compared with healthy control participants, patients with prediabetes or diabetes presented lower ε s and ε e values and a relatively preserved LACI. Patients with diabetes presented considerably reduced SRs, SRe, and LAPEmF. Elevated glycated haemoglobin (HbA1c) levels were independently associated with decreased magnitudes of ε s , SRs, ε e , and SRe. No significant associations were found between the LACI and the HbA1c or LA deformation parameters. We observed significant correlations between LATEmF and ε s , LAPEmF and ε e and between LAAEmF and ε a . Conclusions CMR-FT provides a potential noninvasive approach for the early detection of alterations in the LA reservoir and conduit function in individuals with prediabetes and diabetes. Graphical Abstract

There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.

In this paper, we propose a method for online upper body tracking using an IP PTZ camera. This type of camera uses a built-in Web server resulting in variable response times when sending control commands. Furthermore, communicating with a Web server involves network delays. Thus, because the camera is inside a control loop, the effective frame rate that can be processed by a computer vision method is irregular and in general low (2–6 fps). Our tracking method has been specifically designed to perform in such conditions. It detects, at every frame, candidate blobs using motion detection, region sampling, and region color appearance. The target is detected among candidate blobs using a fuzzy classifier. Then, a movement command is sent to the camera using the target position and speed. The proposed method can cope with low frame rate, and thus with large motion of the target, even in the case of a fast walk. Results show that our system has a good target detection precision (>88%) and low track fragmentation, and the target is almost always localized within 1/6th of the image diagonal from the image center.

Emerging scientific simulations on leadership class systems are generating huge amounts of data and processing this data in an efficient and timely manner is critical for generating insights from the simulations. However, the increasing gap between computation and disk I/O speeds makes traditional data analytics pipelines based on post-processing cost prohibitive and often infeasible. In this paper, we investigate an alternate approach that aims to bring the analytics closer to the data using in-situ execution of data analysis operations. Specifically, we present the design, implementation and evaluation of a framework that can support in-situ feature-based objects tracking on distributed scientific datasets. Central to this framework is a scalable decentralized and online clustering, a cluster tracking algorithm, which executes in-situ (on different cores) in parallel with the simulation processes, and retrieves data from the simulations directly via on-chip shared memory. The results from our experimental evaluation demonstrate that the in-situ approach significantly reduces the cost of data movement, that the presented framework can support scalable feature-based objects tracking, and that it can be effectively used for in-situ analytics in large scale simulations.

This paper presents how effective it is to use many features for improving the speed and accuracy of visual servo systems. Some rank conditions which relate the image Jacobian to the control performance are derived. The focus is to describe that the accuracy of the camera position control in the world coordinate system is increased by utilizing redundant features in this paper. It is also proven that the accuracy is improved by increasing the number of features involved. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm Robot manipulator made by Samsung Electronic Co. Ltd..

Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted from convolutional neural networks in continuous object tracking. The spatial features retain higher resolution for precise localization and semantic features capture more semantic information and less fine-grained spatial details. Therefore, we localize the target by fusing these different features, which improves the tracking accuracy. Besides, we construct the multi-scale pyramid correlation filter of the target and extract its spatial features. This filter determines the scale level effectively and tackles target scale estimation. Finally, we further present a novel model updating strategy, and exploit peak sidelobe ratio (PSR) and skewness to measure the comprehensive fluctuation of response map for efficient tracking performance. Each contribution above is validated on 50 image sequences of tracking benchmark OTB-2013. The experimental comparison shows that our algorithm performs favorably against 12 state-of-the-art trackers.

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

Most existing trackers based on RGB/grayscale frames may collapse due to the unreliability of conventional sensors in some challenging scenarios (e.g., motion blur and high dynamic range). Event-based cameras as bioinspired sensors encode brightness changes with high temporal resolution and high dynamic range, thereby providing considerable potential for tracking under degraded conditions. Nevertheless, events lack the fine-grained texture cues provided by RGB/grayscale frames. This complementarity encourages us to fuse visual cues from the frame and event domains for robust object tracking under various challenging conditions. In this paper, we propose a novel event feature extractor to capture spatiotemporal features with motion cues from event-based data by boosting interactions and distinguishing alterations between states at different moments. Furthermore, we develop an effective feature integrator to adaptively fuse the strengths of both domains by balancing their contributions. Our proposed module as the plug-in can be easily applied to off-the-shelf frame-based trackers. We extensively validate the effectiveness of eight trackers extended by our approach on three datasets: EED, VisEvent, and our collected frame-event-based dataset FE141. Experimental results also show that event-based data is a powerful cue for tracking.

In this paper we present a vehicle detection and tracking method for traffic video analysis based on deep learning technology. Indeed, with the rapid development of deep neural networks, vision-based approaches for vehicle tracking by detection have significantly advanced compared to existing approaches. Therefore, the proposed method is composed of three deep neural networks: Feature Network, Region Proposal Network (RPN) and detection network. The Feature Network is used to pre-train and convert video frame to feature maps using a specific convolutional neural network. The RPN network is a an additional convolutional neural network that slides on the feature map and provides a set of bounding boxes that has high probability of containing any object. Finally, a detection Network based on Region-based Convolutional Neural Network (R-CNN) is in charge of assigning a class and bounding box to each region of interest. The main idea of object tracking is to use region of interest for frame-by-frame tracking by extracting features from the current frame then using object detection from the previous frame to regress their detections in the current frame. Experiment results prove that the proposed method provide a high accuracy rate compared with existing methods.