Computer vision based indoor localization methods use either an infrastructure of static
cameras to track mobile entities (eg, people, robots) or cameras attached to the mobile …

Although deep-learning based methods for monocular pedestrian detection have made
great progress, they are still vulnerable to heavy occlusions. Using multi-view information …

To reduce human efforts in browsing long surveillance videos, synopsis videos are
proposed. Traditional synopsis video generation applying optimization on video tubes is …

To accurately localize occluded people in a crowd is a challenging problem in video
surveillance. Existing end-to-end deep multi-camera detectors rely heavily on pre-training …

Multi-camera pedestrian detection is the challenging problem in the field of surveillance
video analysis. However, existing approaches may produce “phantoms”(ie, fake …

Although surprisingly good progress has been made in monocular pedestrian detection with
emerging deep learning techniques, the existing algorithms still suffer from heavy occlusion …

In the current worldwide situation, pedestrian detection has reemerged as a pivotal tool for
intelligent video-based systems aiming to solve tasks such as pedestrian tracking, social …

Multiple cameras have been frequently used to detect heavily occluded pedestrians. The
state-of-the-art methods, for deep multi-view pedestrian detection, usually project the feature …

A human localization system using multi-source heterogeneous data in indoor environments
is proposed in this paper. The system can be easily constructed with already deployed Wi-Fi …

Urban search and rescue missions require rapid intervention to locate victims and survivors
in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) …