Accurate Long-Term Multiple People Tracking Using Video and Body-Worn IMUs

Most modern approaches for video-based multiple people tracking rely on human appearance to exploit similarities between person detections. Consequently, tracking accuracy degrades if this kind of information is not discriminative or if people change apparel. In contrast, we present a method to fuse video information with additional motion signals from body-worn inertial measurement units (IMUs). In particular, we propose a neural network to relate person detections with IMU orientations, and formulate a graph labeling problem to obtain a tracking solution that is globally consistent with the video and inertial recordings. The fusion of visual and inertial cues provides several advantages. The association of detection boxes in the video and IMU devices is based on motion, which is independent of a person's outward appearance. Furthermore, inertial sensors provide motion information irrespective of visual occlusions. Hence, once detections in the video are associated with an IMU device, intermediate positions can be reconstructed from corresponding inertial sensor data, which would be unstable using video only. Since no dataset exists for this new setting, we release a dataset of challenging tracking sequences, containing video and IMU recordings together with ground-truth annotations. We evaluate our approach on our new dataset, achieving an average IDF1 score of 91.2%. The proposed method is applicable to any situation that allows one to equip people with inertial sensors. © 1992-2012 IEEE

15-06 Integrated Crowdsourcing Platform to Investigate Non-Motorized Behavior and Risk Factors on Walking, Running, and Cycling Routes

There are several factors on the roads that impact bicyclists’ safety. This research aims to find the most important risk factors on roads, mainly in infrastructure facilities, to improve the safety for walkers, runners, and bicyclists. Most mobile cycling applications currently used by cyclists and runners were reviewed in this study in order to gain insight about the features that users care about. Features, such as speed, cumulative elevation gain, and connectivity to Google Fit, were found to be the most common features in the widely-used cycling apps. In this research, we developed and launched a mobile application for crowd-sourcing of roads’ risk factors. With the proposed application, some of the cycling risk factors can be mitigated. We launched the BikeableRoute mobile application allowing bicyclists to share reports of hazards encountered on roads with other fellow bicyclists and the local authorities. To achieve the goals of this study, the mobile application collects anonymous data and self-reported risk factors and biking data. This study allows collecting user’s data for later processing to extract knowledge and insight. Our proposed system enables local authorities to operate more efficiently to handle the feedback provided by the citizens. Also, the local government will be able to provide statistical reports that provide estimates of the traffic on the different routes throughout the local community

Adaptive Indoor Pedestrian Tracking Using Foot-Mounted Miniature Inertial Sensor

This dissertation introduces a positioning system for measuring and tracking the momentary location of a pedestrian, regardless of the environmental variations. This report proposed a 6-DOF (degrees of freedom) foot-mounted miniature inertial sensor for indoor localization which has been tested with simulated and real-world data. To estimate the orientation, velocity and position of a pedestrian we describe and implement a Kalman filter (KF) based framework, a zero-velocity updates (ZUPTs) methodology, as well as, a zero-velocity (ZV) detection algorithm. The novel approach presented in this dissertation uses the interactive multiple model (IMM) filter in order to determine the exact state of pedestrian with changing dynamics. This work evaluates the performance of the proposed method in two different ways: At first a vehicle traveling in a straight line is simulated using commonly used kinematic motion models in the area of tracking (constant velocity (CV), constant acceleration (CA) and coordinated turn (CT) models) which demonstrates accurate state estimation of targets with changing dynamics is achieved through the use of multiple model filter models. We conclude by proposing an interactive multiple model estimator based adaptive indoor pedestrian tracking system for handling dynamic motion which can incorporate different motion types (walking, running, sprinting and ladder climbing) whose threshold is determined individually and IMM adjusts itself adaptively to correct the change in motion models. Results indicate that the overall IMM performance will at all times be similar to the best individual filter model within the IMM

Traveled Distance Estimation Algorithm for Indoor Localization

This paper presents an ankle mounted Inertial Navigation System (INS) used to estimate the distance traveled by a pedestrian. This distance is estimated by the number of steps given by the user. The proposed method is based on force sensors to enhance the results obtained from an INS. Experimental results have shown that, depending on the step frequency, the traveled distance error varies between 2.7% and 5.6%