Search Results for author:
Found 26 papers, 15 papers with code
Learning Data Association for Multi-Object Tracking using Only Coordinates
We propose a novel Transformer-based module to address the data association problem for multi-object tracking.
Detection of Micromobility Vehicles in Urban Traffic Videos
Urban traffic environments present unique challenges for object detection, particularly with the increasing presence of micromobility vehicles like e-scooters and bikes.
Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting
Deep learning-based multivariate and multistep-ahead traffic forecasting models are typically trained with the mean squared error (MSE) or mean absolute error (MAE) as the loss function in a sequence-to-sequence setting, simply assuming that the errors follow an independent and isotropic Gaussian or Laplacian distributions.
Laplacian Convolutional Representation for Traffic Time Series Imputation
In this study, we first introduce a Laplacian kernel to temporal regularization for characterizing local trends in traffic time series, which can be formulated as a circular convolution.
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
In the temporal context, the complex time-varying system behaviors can be revealed by the temporal modes in the proposed model.
ActAR: Actor-Driven Pose Embeddings for Video Action Recognition
Human action recognition (HAR) in videos is one of the core tasks of video understanding.
Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting
Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary.
Trajectory Clustering Performance Evaluation: If we know the answer, it's not clustering
Finally, we use a combination of evaluation measures to find the top performing similarity measures and clustering algorithms for each intersection.
PolyTrack: Tracking with Bounding Polygons
In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons.
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
Multi-Object Tracking and Segmentation with a Space-Time Memory Network
We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets.
FFAVOD: Feature Fusion Architecture for Video Object Detection
We propose FFAVOD, standing for feature fusion architecture for video object detection.
Ranked #2 on
Object Detection
on UA-DETRAC
CenterPoly: real-time instance segmentation using bounding polygons
The models were trained and evaluated on Cityscapes, KITTI and IDD and the results are reported on their public benchmark, which are state-of-the-art at real-time speeds.
Ranked #1 on
Real-time Instance Segmentation
on Cityscapes test
MeNToS: Tracklets Association with a Space-Time Memory Network
We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection.
Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?
In this paper, we focus on the deep-learning models with a soft-attention mechanism for social interaction modeling and study whether they use social information at prediction time.
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor.
A Grid-based Representation for Human Action Recognition
Human action recognition (HAR) in videos is a fundamental research topic in computer vision.
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes
Commonly used features are color histograms, histograms of oriented gradients, deep features from convolutional neural networks and re-identification (ReID) features.
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputation tasks by effectively characterizing the complex correlations/dependencies in spatiotemporal data.
Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study
In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework.
RN-VID: A Feature Fusion Architecture for Video Object Detection
Consecutive frames in a video are highly redundant.
Ranked #4 on
Object Detection
on UAVDT
SpotNet: Self-Attention Multi-Task Network for Object Detection
We use those segmentation maps inside the network as a self-attention mechanism to weight the feature map used to produce the bounding boxes, decreasing the signal of non-relevant areas.
Ranked #3 on
Object Detection
on UA-DETRAC
Autocamera Calibration for traffic surveillance cameras with wide angle lenses
Extrinsic calibration is accomplished by estimating the two vanishing points, on the ground plane, from the motion of vehicles at perpendicular intersections.
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection
In this paper, we propose to combine detections from background subtraction and from a multiclass object detector for multiple object tracking (MOT) in urban traffic scenes.
Road User Detection in Videos
Two new models, RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the concatenation of a target frame with a preceding frame, and the concatenation of the optical flow with the target frame.
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector
Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes.
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing
In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking.
