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Found 30 papers, 12 papers with code
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
Vehicle Behavior Prediction by Episodic-Memory Implanted NDT
In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev.
Vision-Based Traffic Accident Detection and Anticipation: A Survey
We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA.
Gated Driver Attention Predictor
In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP).
Enhancing Mapless Trajectory Prediction through Knowledge Distillation
Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents.
Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving
One major issue in learning-based model predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction accuracy and computation efficiency.
UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment.
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction.
Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
In particular, the text description provides a dense semantic description guidance for the primary context of the traffic scene, while the driver attention provides a traction to focus on the critical region closely correlating with safe driving.
Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction
To this end, we formulate a deep virtual to real distillation framework by introducing the synthetic data that can be generated conveniently, and borrow the abundant information of pedestrian movement in synthetic videos for the pedestrian crossing prediction in real data with a simple and lightweight implementation.
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints.
Behavioral Intention Prediction in Driving Scenes: A Survey
Behavioral Intention Prediction (BIP) simulates such a human consideration process and fulfills the early prediction of specific behaviors.
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU.
Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences
The proposed sparse semantic map-based localization approach is robust against occlusion and long-term appearance changes in the environments.
Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding
Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories.
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems.
DADA: Driver Attention Prediction in Driving Accident Scenarios
1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks
For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i. e., attention, to each element/dimension of the input.
Ranked #3 on
Skeleton Based Action Recognition
on SYSU 3D
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations.
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data.
Ranked #1 on
Skeleton Based Action Recognition
on SYSU 3D
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction.
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism.
Adding Attentiveness to the Neurons in Recurrent Neural Networks
We propose adding a simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block (e. g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability.
Ranked #98 on
Skeleton Based Action Recognition
on NTU RGB+D
Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of \emph{bursty} features.
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.
Ranked #1 on
Skeleton Based Action Recognition
on UWA3D
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting
We develop a unified framework for complex event retrieval, recognition and recounting.
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Ranked #6 on
Skeleton Based Action Recognition
on SYSU 3D
Counting Grid Aggregation for Event Retrieval and Recognition
Event retrieval and recognition in a large corpus of videos necessitates a holistic fixed-size visual representation at the video clip level that is comprehensive, compact, and yet discriminative.
Illumination Robust Color Naming via Label Propagation
In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically.
