Search Results for author:
Found 12 papers, 2 papers with code
Joint multi-person detection and tracking from overlapping cameras
We present a system to track the positions of multiple persons in a scene from overlapping cameras.
The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
The dataset furthermore contains a large number of person orientation annotations (over 211200).
Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information.
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important.
CNN based Road User Detection using the 3D Radar Cube
In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0. 70 (baseline: 0. 68) target-wise and 0. 56 (baseline: 0. 48) object-wise F1 score.
Diversity in Action: General-Sum Multi-Agent Continuous Inverse Optimal Control
Traffic scenarios are inherently interactive.
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data
We show that this continuous representation is suitable to encode geometric and semantic properties of extensive outdoor scenes without the need for spatial discretization (thus avoiding the trade-off between level of scene detail and the scene extent that can be covered).
Ranked #5 on
3D Semantic Scene Completion
on SemanticKITTI
Structural Knowledge Distillation for Object Detection
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities.
Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning.
Multimodal Object Query Initialization for 3D Object Detection
In experiments, we outperform the state of the art in transformer-based LiDAR object detection on the competitive nuScenes benchmark and showcase the benefits of input-dependent multimodal query initialization, while being more efficient than the available alternatives for LiDAR-camera initialization.
See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar
In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets.
