Indoor Trajectory Tracking Research Articles

Predicting of a person's position in trajectory tracking from a continuous video stream

The paper proposes a method for predicting when a person enters a forbidden zone during his trajectory following a video stream, considering individual body parts. The authors used the PP-TinyPose PaddleHub neural network model with its implementation based on two deep neural networks to detect key points of the human body. The paper considers an example of human position prediction from a continuous video stream in indoor trajectory tracking. The authors predicted each key point in the coordinate space of the video stream using a recurrent deep neural network algorithm.

Improving Room-Level Location for Indoor Trajectory Tracking with Low IPS Accuracy

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

Matrix Structure Driven Interior Point Method for Quadrotor Real-Time Trajectory Planning

Sequential convex programming (SCP) has been recently employed in various trajectory planning problems, including entry flight, planetary landing, and aircraft formation. In SCP, convex programming subproblems are sequentially solved to obtain the optimum of original nonconvex problems. For SCP-based quadrotor trajectory planning, this paper proposes a matrix-structure-driven interior point method (MSD-IPM) to improve the efficiency of solving search directions in convex programming. In MSD-IPM, primal-dual systems for solving search directions are derived from the Karush-Kuhn-Tucher (KKT) conditions of quadrotor trajectory planning subproblems. Then, the successive elimination technique is used to solve the inverse of large-scale coefficient matrices of primal-dual systems by more efficient operations on small-scale matrices. In successive elimination, the positive definiteness of several small-scale matrices is used to enhance the numerical stability of computing search directions, and the specific diagonal structures of small-scale matrices are exploited to efficiently compute the search directions. The complexity analysis shows that the efficiency of the proposed method is about one order of magnitude higher than that of the standard IPM. The comparative studies on simulation experiments demonstrate that the MSD-IPM generally outperforms several well-known optimizers (e.g., MOSEK, SDPT3, and SeDuMi) in terms of efficiency and robustness. Finally, the indoor trajectory tracking experiments indicate that the proposed method can generate smooth trajectories for real-world applications.

A Novel Semantic Matching Method for Indoor Trajectory Tracking

The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., “Turn left”, “Turn right”, and “Go straight”). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator (RSSI) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object’s trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

A miniature shoe-mounted orientation determination system for accurate indoor heading and trajectory tracking.

Tracking the position of pedestrian is urgently demanded when the most commonly used GPS (Global Position System) is unavailable. Benefited from the small size, low-power consumption, and relatively high reliability, micro-electro-mechanical system sensors are well suited for GPS-denied indoor pedestrian heading estimation. In this paper, a real-time miniature orientation determination system (MODS) was developed for indoor heading and trajectory tracking based on a novel dual-linear Kalman filter. The proposed filter precludes the impact of geomagnetic distortions on pitch and roll that the heading is subjected to. A robust calibration approach was designed to improve the accuracy of sensors measurements based on a unified sensor model. Online tests were performed on the MODS with an improved turntable. The results demonstrate that the average RMSE (root-mean-square error) of heading estimation is less than 1°. Indoor heading experiments were carried out with the MODS mounted on the shoe of pedestrian. Besides, we integrated the existing MODS into an indoor pedestrian dead reckoning application as an example of its utility in realistic actions. A human attitude-based walking model was developed to calculate the walking distance. Test results indicate that mean percentage error of indoor trajectory tracking achieves 2% of the total walking distance. This paper provides a feasible alternative for accurate indoor heading and trajectory tracking.