-
4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras
This paper contributes a novel realtime multi-person motion capture algo...
read it
-
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
The task of multiple people tracking in monocular videos is challenging ...
read it
-
What Makes Kevin Spacey Look Like Kevin Spacey
We reconstruct a controllable model of a person from a large photo colle...
read it
-
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
Robust online multi-person tracking requires the correct associations of...
read it
-
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
We present a data association method for vision-based multiple pedestria...
read it
-
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Multi-Person Tracking (MPT) is often addressed within the detection-to-a...
read it
-
Online People Tracking and Identification with RFID and Kinect
We introduce a novel, accurate and practical system for real-time people...
read it
Automatic Adaptation of Person Association for Multiview Tracking in Group Activities
Reliable markerless motion tracking of multiple people participating in complex group activity from multiple handheld cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. The key to solving this problem is to reliably associate the same person across distant viewpoint and temporal instances. In this work, we combine motion tracking, mutual exclusion constraints, and multiview geometry in a multitask learning framework to automatically adapt a generic person appearance descriptor to the domain videos. Tracking is formulated as a spatiotemporally constrained clustering using the adapted person descriptor. Physical human constraints are exploited to reconstruct accurate and consistent 3D skeletons for every person across the entire sequence. We show significant improvement in association accuracy (up to 18 in events with up to 60 people and 3D human skeleton reconstruction (5 to 10 times) over the baseline for events captured "in the wild".
READ FULL TEXT
Comments
There are no comments yet.