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Found 33 papers, 6 papers with code
Instance Flow Based Online Multiple Object Tracking
The evaluation shows that our tracking approach is able to track objects with high relative motions.
3D Trajectory Reconstruction of Dynamic Objects Using Planarity Constraints
We apply Structure from Motion techniques to object and background images to determine for each frame camera poses relative to object instances and background structures.
Particle-based pedestrian path prediction using LSTM-MDL models
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space.
An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark
In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks.
Stereo 3D Object Trajectory Reconstruction
We compute the object trajectory by combining object and background camera pose information.
3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints
We apply Structure from Motion techniques to vehicle and background images to determine for each frame camera poses relative to vehicle instances and background structures.
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants.
An RNN-based IMM Filter Surrogate
The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation.
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals
In this paper, several variants of two-stream architectures for temporal action proposal generation in long, untrimmed videos are presented.
Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures
Towards this end, a neural network model for continuous-time stochastic processes usable for sequence prediction is proposed.
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data.
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition
Therefore, it is necessary for such a learning-based system in a real world environment, to be aware of its own capabilities and limits and to be able to distinguish between confident and unconfident results of the inference, especially if the sample cannot be explained by the underlying distribution.
A Short Note on Analyzing Sequence Complexity in Trajectory Prediction Benchmarks
The analysis and quantification of sequence complexity is an open problem frequently encountered when defining trajectory prediction benchmarks.
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models.
Integration of the 3D Environment for UAV Onboard Visual Object Tracking
The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking.
A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking
We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking.
3D Surface Reconstruction From Multi-Date Satellite Images
The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry.
Handling Missing Observations with an RNN-based Prediction-Update Cycle
For providing a full temporal filtering cycle, a basic RNN is extended to take observations and the associated belief about its accuracy into account for updating the current state.
MODISSA: a multipurpose platform for the prototypical realization of vehicle-related applications using optical sensors
We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection.
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction
By providing missing tokens, binary-encoded missing events, the model learns to in-attend to missing data and infers a complete trajectory conditioned on the remaining inputs.
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset.
A Comparison of Deep Saliency Map Generators on Multispectral Data in Object Detection
The dataset used in this work is the Multispectral Object Detection Dataset, where each scene is available in the FIR, MIR and NIR as well as visual spectrum.
Improving Semantic Image Segmentation via Label Fusion in Semantically Textured Meshes
Models for semantic segmentation require a large amount of hand-labeled training data which is costly and time-consuming to produce.
Revisiting Click-based Interactive Video Object Segmentation
While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Video Object Segmentation (CiVOS) framework to simplify the required user workload as much as possible.
Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors
Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
Bézier Curve Gaussian Processes
Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information.
Uncertainty-aware Vision-based Metric Cross-view Geolocalization
This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose.
READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation
We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstrained videos.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
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Geo-Tiles for Semantic Segmentation of Earth Observation Imagery
This allows us to perform a tile specific data augmentation during training and a substitution of pixel predictions with limited context information using data of overlapping tiles during inference.
Eigenpatches -- Adversarial Patches from Principal Components
The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector.
Utilizing dataset affinity prediction in object detection to assess training data
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive.
C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation
To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match.
Generating Synthetic Ground Truth Distributions for Multi-step Trajectory Prediction using Probabilistic Composite Bézier Curves
An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks.
