The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.

Road crack detection using deep convolutional neural network

Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In addition, our method is faster and easier to parallel. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.

Automatic Road Crack Detection Using Random Structured Forests

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

https://nottingham-repository.worktribe.com/preview/745918/Manuscript_KochEtAl_2015_accepted.pdf

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

https://www.cse.sc.edu/~songwang/document/prl12.pdf

CrackTree: Automatic crack detection from pavement images

FoSA: F* Seed-growing Approach for crack-line detection from pavement images

This paper presents an activity sequence-based indoor pedestrian localization approach using smartphones. The activity sequence consists of several continuous activities during the walking process, such as turning at a corner, taking the elevator, taking the escalator, and walking stairs. These activities take place when a user walks at some special points in the building, like corners, elevators, escalators, and stairs. The special points form an indoor road network. In our approach, we first detect the user's activities using the built-in sensors in a smartphone. The detected activities constitute the activity sequence. Meanwhile, the user's trajectory is reckoned by Pedestrian Dead Reckoning (PDR). Based on the detected activity sequence and reckoned trajectory, we realize pedestrian localization by matching them to the indoor road network using a Hidden Markov Model. After encountering several special points, the location of the user would converge on the true one. We evaluate our proposed approach using smartphones in two buildings: an office building and a shopping mall. The results show that the proposed approach can realize autonomous pedestrian localization even without knowing the initial point in the environments. The mean offline localization error is about 1.3 m. The results also demonstrate that the proposed approach is robust to activity detection error and PDR estimation error.

Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones

Mining attractive areas that people interested in and their related movement patterns can lead to instructive insight to transport management, urban planning and location-based services (LBS). The number of visiting that it attracts is used in this paper to measure an area's level of attractiveness (LoA). As one of the most widely used mode of transport, taxi can tell a lot of stories about not only road network traffic condition, but also areas people interested in crossing a day and their related travel patterns, such as travel destination and average travel distance. Conventional taxi trajectory analysis, or more generally, probe vehicle and floating car trajectory analysis, more focuses on road network travel time and average speed estimation. This study from another angle, uses taxi trajectory data to discover attractive areas that people often visit, for instance, hot shopping and leisure places or living and working areas based on their LoA which hereby is represented as the frequency and density of passenger pick-up and drop-off points, because each point represents a certain scope where attractiveness generates. To obtain meaningful patterns, clustering approach is used to group spatiotemporally similar pick-up and drop-off points, because people's interests to these areas varies through time of the day, day of the week, even season of the year. Moreover, a time-dependent travel flow interaction matrix is established, which is a variation of O-D (Origin-Destination) matrix used in transport domain, and can be used to better understand movement patterns by quantizing the attractiveness among clusters. Background geographic information is used to facilitate the understanding of the movement. This study represents a novel application of taxi trajectory data, reveals people's travel demand and movement patterns in a more deep sense to serve transport management, urban planning, as well as spatiotemporal-tailored location search and services.

Mining time-dependent attractive areas and movement patterns from taxi trajectory data

Indoor maps are integral to pedestrian navigation systems, an essential element of intelligent transportation systems (ITS). In this paper, we propose ALIMC, i.e., Activity Landmark-based Indoor Mapping system via Crowdsourcing. ALIMC can automatically construct indoor maps for anonymous buildings without any prior knowledge using crowdsourcing data collected by smartphones. ALIMC abstracts the indoor map using a link–node model in which the pathways are the links and the intersections of the pathways are the nodes, such as corners, elevators, and stairs. When passing through the nodes, pedestrians do the corresponding activities, which are detected by smartphones. After activity detection, ALIMC extracts the activity landmarks from the crowdsourcing data and clusters the activity landmarks into different clusters, each of which is treated as a node of the indoor map. ALIMC then estimates the relative distances between all the nodes and obtains a distance matrix. Based on the distance matrix, ALIMC generates a relative indoor map using the multidimensional scaling technique. Finally, ALIMC converts the relative indoor map into an absolute one based on several reference points. To evaluate ALIMC, we implement ALIMC in an office building. Experiment results show that the 80th percentile error of the mapping accuracy is about 0.8–1.5 m.

Qingzhou Mao

Papers

CrackTree: Automatic crack detection from pavement images

FoSA: F* Seed-growing Approach for crack-line detection from pavement images

Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones

Mining time-dependent attractive areas and movement patterns from taxi trajectory data

ALIMC: Activity Landmark-Based Indoor Mapping via Crowdsourcing