The development of Dense Crowd Visual Tracking algorithm based on Deep Learning
(DTDL) is introduced. The main research contents of this paper are as follows:(l) Dense …

We present a multiple-person tracking algorithm, based on combining particle filters (PFs)
and reciprocal velocity obstacle (RVO), an agent-based crowd model that infers collision …

In recent years, the demand for intelligent devices related to the Internet of Things (IoT) is
rapidly increasing. In the field of computer vision, many algorithms have been preinstalled in …

Artificial intelligence-based surveillance system, one of the essential systems for smart cities,
plays a critical role in ensuring the safety and well-being of individuals. In this paper, we …

In mobile crowd sensing, task allocation is of vital importance, and it has attracted much
attention in recent years. Though there have been many studies focusing on task allocation …

In this paper, we study the problem of detecting and tracking multiple objects of various
types in outdoor urban traffic scenes. This problem is especially challenging due to the large …

We study the problem of aggregating the contributions of multiple contributors in a
crowdsourcing setting. The data involved is in a form not typically considered in most …

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task
allocation approaches follow either the opportunistic or participatory mode, this paper …

Abstract This paper proposes Motion Structure Tracker (MST) to solve the problem of
tracking in very crowded structured scenes. It combines visual tracking, motion pattern …

In recent years, several methods and datasets have been proposed to push the performance
of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors …