Efficient viewpoint assignment for urban texture documentation

Abstract

We envision participatory texture documentation (PTD) as a process in which a group of users (dedicated individuals and/or general public) with camera-equipped mobile phones participate in collaborative collection of urban texture information. PTD enables inexpensive, scalable and high resolution urban texture documentation. We have proposed to implement PTD in two steps [10]. At the first step, termed viewpoint selection, a minimum number of points in the urban environment are selected from which the texture of the entire urban environment (the part visible to cameras) can be collected/captured. At the second step, called viewpoint assignment, the selected viewpoints are assigned to the participating users such that given a limited number of users with various constraints (e.g., restricted available time) users can collectively capture the maximum amount of texture information within a limited time interval. In this paper, we focus on the viewpoint assignment problem. We first prove that this problem is an NP-hard problem, and therefore, the optimal solution for viewpoint assignment fails to scale as the extent of the urban environment and the number of participating users grow. Subsequently, we propose a family of heuristics for efficient viewpoint assignment to reduce the assignment running time while ensuring an almost complete texture collection. We study, profile and verify our proposed solutions comparatively by both rigorous analysis and extensive experiments.

Spatial Task Assignment for Crowd Sensing with Cloaked Locations

“…The criteria for optimization of task assignment include sensing costs, coverage of targets of interest, quality, and credibility of sensed data. Examples of this approach can be found in [4], [9], [18], [19]. Reddy et al [9] proposed a coveragebased task assessment that finds the least costly subset of participants to achieve the coverage goal.…”

“…Reddy et al [9] proposed a coveragebased task assessment that finds the least costly subset of participants to achieve the coverage goal. Shirani-Mehr et al [4] also proposed a coverage-based task assignment method for assigning viewpoints to a group of moving participants. None of these studies considers location privacy restrictions.…”

“…Participants might need to travel to the location of the assigned targets to collect data using their smart devices or phones. Examples of these systems include crowd-contributed instant news coverage, trail condition updates after storms [3], and urban texture documentation [4]. This can be also considered as one type of location-aware crowdsourcing applications [5]- [7] in which tasks are distributed to participants with regard to their locations.…”

“…Several projects have focused on the optimization of spatial task assignment to improve the sensing process [4], [9]. However, a major concern of all these systems 1 In this paper, we use participants and workers interchangeably since our methods can be adopted for both voluntary or incentive-based models.…”

Privacy assurance in mobile sensing networks: Go beyond trusted servers

“…The ubiquity of mobile devices has brought the rapid development of mobile sensing networks. In the last few years, mobile participatory sensing systems [4], [7], [9], [14], [25], [17], [21], [22] have attracted the attention of many researchers. GPS-equipped mobile phones carried by humans or vehicles move around urban areas and collect data along their routes.…”