Abstract Gesture recognition is a fundamental technology in nearly all Human-Computer Interaction (HCI) devices, drawing significant attention from engineers and researchers worldwide. Fine-grained air-writing recognition within a small spatial range remains a challenging yet crucial research topic. In this paper, we propose a method, namely RingRT, which uses an inertial measurement unit (IMU) integrated in the ring to identify character air-writing actions within an area as big as the palm of one’s hand. RingRT leverages the neural network that integrates both the Transformer architecture and the Focused Linear Attention mechanism, enabling it to effectively analyze 6-axis motion signals and accurately recognize air-written characters. In experiments, we collected 24,120 air-writing samples within a 6 cm $$\times$$ × 6 cm area, comprising 67 characters from 24 participants across three scenarios. RingRT achieved a user-independent accuracy of 90.08% in offline evaluation and maintained over 86% accuracy in real-world scenarios.

Many oceanographic vehicles, especially those used for long-distance missions and with limited operating speeds, are sensitive to external field influences, such as ocean currents. A feasible, safe, and optimal path is critical for autonomous operation due to the dynamic and intermittent environment of the vehicle and physical constraints. This article develops a path planning algorithm on the basis of the level set method. Specifically, we utilize the second-order alternating evolution method to solve the Hamilton–Jacobi equation with time parameter. In addition, the flow field information is mapped to the level set equation, and the basemap and ocean current information are updated in real time. Meanwhile, to optimize the evolution of the accessible boundary, a second-order polygon is constructed through Newton's mean difference interpolation method. Finally, we can plan the optimal path under the current environment via the accessible boundary information and the path backtracking equation. The goal is to compute the path for the surface vehicle so as to minimize the travel time in the presence of ocean currents. Numerical examples confirm the efficiency of the approach.

A fast sparse covariance-based fitting algorithm with the non-negative least squares (NNLS) form is proposed for the direction of arrival (DOA) estimation. The Khatri-Rao product of the array manifold of the uniform linear arrays is utilized to achieve the dimension reducing transformation after vectorizing the array covariance matrix. Furthermore, the DOA estimation problem is derived as a NNLS problem by using the non-negative property of the spatial spectrum, which can be solved by some efficient solvers. Numerical experiments show that the proposed method can obtain high resolution with a competitive computational complexity, as well as works in the presence of coherent sources.

Traditionally, vehicle tracking is accomplished using predeployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this paper, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photographs, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photographs of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle's actual trajectory and 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 m error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set and experimental results validate its effectiveness and efficiency.

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

Pedestrians distracted by smartphones are easy to meet with various dangers when crossing or walking on the street, such as the obstacles on the sidewalk (e.g., temporary parking and road repairing). Existing works about pedestrian safety are mostly based on the sensing capabilities from a single device. The surrounding information that can be learned, however, is quite limited or incomplete. Therefore, in many cases the dangers cannot be detected and the pedestrians cannot be alerted. In this paper, a novel system called CrowdWatch is proposed, which leverages mobile crowd sensing and crowd intelligence aggregation to detect temporary obstacles and make effective alerts for distracted walkers. To detect obstacles, we first study the regular rules of pedestrians' avoidance behaviors from the aspects of turn-making and visual contexts. The Dempster-Shafer evidence theory is then used to fuse the behavior and visual contexts, and further calculate the confidence of obstacle existence. Afterwards, we leverage the features of pedestrians' traces to characterize an appropriate dangerous area, which is used to alert distracted walkers. The conducted experiments with 36 participants and different obstacle settings indicate that the crowd-intelligencebased obstacle detection method is effective and the accuracy of reminding attains 83.3%.

Visual crowdsensing is successfully applied in numerous application areas, yet little work has been done on measuring and improving the quality of worker contributed visual data. Rather than evaluating the visual quality based on traditional metrics such as resolution, we focus on data diversity, which is crucial for a broad stream of visual crowdsensing tasks. Two representative diversity-oriented task types are studied, namely static object imagery and evolving event photography. The former aims to collect multi-facet/ aspect yet low redundant data about a stationary object, while the latter wants to detect and collect details of key scenes throughout an event. We link these quality needs with data utility and propose a unified visual crowdsensing framework called UtiPay. Data utility is characterized by the macro and micro diversity needs: at the macro level, the pyramid-tree approach is proposed for multi-attribute-based data grouping; at the micro level, we use several strategies for intra-group data selection and worker contribution measurement. To study the impact of our proposed utility measurement approaches, we propose two utility-enhanced payment schemes as incentive mechanisms: Uti and Uti-Bid. Experiments over several user studies with a total of 43 subjects validate the performance of UtiPay for measuring and enhancing the data quality of visual crowdsensing tasks.

Mobile social media enables people to record ongoing physical events they witness and share them instantaneously online. However, since these event pictures are often individually provided, they are typically fragmented and possess high redundancy. Though there have been studies about visual event summarization, they pay little attention to collaborative sensing, subevent detection, and event summary. In this paper, we present several building blocks for a cooperative visual sensing and sharing system. We create a virtual opportunistic community associated with an event, where members collaborate to cover different aspects of the event. More specifically, a crowd-powered approach is first used to localize the event. We then propose three subevent segmentation methods based on crowd-event interaction patterns. Based on the segmentation results, we summarize the event at two levels: multi-facet subevent summary and crowd-behavior-based highlights. Experiments over 21 online datasets and two real world datasets demonstrate the effectiveness of our approaches.

A vehicle's fuel consumption is strongly related to both its loading and the driver's driving behavior, such as aggressive/tender acceleration, improper/proper gear change or running/stopping the engine while waiting. This paper introduces EasyRoute, an economical route recommendation system for modern vehicles, implemented in smartphones, to improve fuel efficiency. EasyRoute senses the vehicle's fuel consumption through the On-Board Diagnostics (OBD) adapter and then models the driver's personal fuel consumption according to OBD data from two aspects: i) when the vehicle is moving and ii) when the vehicle is idling or waiting. Based on the crowdsourced traffic information, EasyRoute can near-correctly predict total fuel consumptions of different routes and recommend drivers with the greenest route. We describe the EasyRoute framework and evaluate it by collecting OBD and GPS data from 559 taxis in Beijing. Comparing with some commonly used baselines with error metrics, the experimental results show that using a small 10-minute dataset for training, the total fuel consumption estimated by EasyRoute has a relative error of at least 30% less than the baselines.

Community bulletin boards serve an important function for public information sharing in modern society. Posted fliers advertise services, events, and other announcements. However, fliers posted offline suffer from problems such as limited spatial-temporal coverage and inefficient search support. In recent years, with the development of sensor-enhanced mobile devices, mobile crowd sensing (MCS) has been used in a variety of application areas. This paper presents FlierMeet, a crowd-powered sensing system for cross-space public information reposting, tagging, and sharing. The tags learned are useful for flier sharing and preferred information retrieval and suggestion. Specifically, we utilize various contexts (e.g., spatio-temporal info, flier publishing/reposting behaviors, etc.) and textual features to group similar reposts and classify them into categories. We further identify a novel set of crowd-object interaction hints to predict the semantic tags of reposts. To evaluate our system, 38 participants were recruited and 2,035 reposts were captured during an eight-week period. Experiments on this dataset showed that our approach to flier grouping is effective and the proposed features are useful for flier category/semantic tagging.