112 citations · 327 across the 17 of their papers we have counts for
16 papers · 1 filter
Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction
Jie Bai, Xin Fang, Jianwu Fang +2
Pedestrian crossing is one of the most typical behavior which conflicts with natural driving behavior of vehicles. Consequently, pedestrian crossing prediction is one of the primar…
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
Xingyu Chen, Jianru Xue, Jianwu Fang +2
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 r…
Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences
Xingyu Chen, Jianru Xue, Shanmin Pang
Vision-based localization in a prior map is of crucial importance for autonomous vehicles. Given a query image, the goal is to estimate the camera pose corresponding to the prior m…
Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding
Pu Zhang, Lei Bai, Jianru Xue +3
Trajectory forecasting is critical for autonomous platforms to make safe planning and actions. Currently, most trajectory forecasting methods assume that object trajectories have b…
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Pengfei Zhang, Cuiling Lan, Wenjun Zeng +3
Skeleton data is of low dimension. However, there is a trend of using very deep and complicated feedforward neural networks to model the skeleton sequence without considering the c…
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Rixing Zhu, Jianwu Fang, Hongke Xu +2
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems. Vehicle Re-ID suffers the numerous challeng…
DADA: Driver Attention Prediction in Driving Accident Scenarios
Jianwu Fang, Dingxin Yan, Jiahuan Qiao +2
Driver attention prediction is becoming an essential research problem in human-like driving systems. This work makes an attempt to predict the driver attention in driving accident…
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks
Pengfei Zhang, Jianru Xue, Cuiling Lan +3
Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on c…
DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark
Jianwu Fang, Dingxin Yan, Jiahuan Qiao +3
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)…
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Pengfei Zhang, Cuiling Lan, Wenjun Zeng +3
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data. Recently, there is a trend of using very deep feed…
BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
Jianru Xue, Jianwu Fang, Tao Li +4
In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, th…
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
Pu Zhang, Wanli Ouyang, Pengfei Zhang +2
In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty…
Adding Attentiveness to the Neurons in Recurrent Neural Networks
Pengfei Zhang, Jianru Xue, Cuiling Lan +3
Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information. However, the structures of existing RNN neurons mainly focus on co…
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
Pengfei Zhang, Cuiling Lan, Junliang Xing +3
Skeleton-based human action recognition has recently attracted increasing attention thanks to the accessibility and the popularity of 3D skeleton data. One of the key challenges in…
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
Pengfei Zhang, Cuiling Lan, Junliang Xing +3
Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations…
Counting Grid Aggregation for Event Retrieval and Recognition
Zhanning Gao, Gang Hua, Dongqing Zhang +2
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…