Search Results for author:
Found 12 papers, 1 papers with code
Single-Network Whole-Body Pose Estimation
We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints.
Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields
We present an online approach to efficiently and simultaneously detect and track the 2D pose of multiple people in a video sequence.
Ranked #7 on
Pose Tracking
on PoseTrack2017
(using extra training data)
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision.
Ranked #12 on
Crowd Counting
on UCF-QNRF
Large-scale Image Geo-Localization Using Dominant Sets
We cast the geo-localization as a clustering problem on local image features.
Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints
This paper addresses the problem of human re-identification across non-overlapping cameras in crowds. Re-identification in crowded scenes is a challenging problem due to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras.
Online Localization and Prediction of Actions and Interactions
For online prediction of action (interaction) confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time progresses.
Predicting the Where and What of Actors and Actions Through Online Action Localization
This paper proposes a novel approach to tackle the challenging problem of 'online action localization' which entails predicting actions and their locations as they happen in a video.
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos.
Action Localization in Videos Through Context Walk
Context relations are learned during training which capture displacements from all the supervoxels in a video to those belonging to foreground actions.
Improving Semantic Concept Detection through the Dictionary of Visually-distinct Elements
A video captures a sequence and interactions of concepts that can be static, for instance, objects or scenes, or dynamic, such as actions.
NMF-KNN: Image Annotation using Weighted Multi-view Non-negative Matrix Factorization
Such models become obsolete and require relearning when new images and tags are added to database.
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region.
Ranked #19 on
Crowd Counting
on UCF-QNRF
