We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios.
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on 3D Object Detection on KITTI Cars Hard val
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on Semantic Segmentation on RELLIS-3D Dataset
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog.
We address the problem of ego-vehicle navigation in dense simulated traffic environments populated by road agents with varying driver behaviors.
We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments.
Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake".
We report an AP of 65. 83 across 4 categories on GroupWalk, which is also an improvement over prior methods.
Ranked #1 on Emotion Recognition in Context on EMOTIC
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
Ranked #1 on Trajectory Prediction on ApolloScape
For the annotated data, we also train a classifier to map the latent embeddings to emotion labels.
Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities.
We use hundreds of annotated real-world gait videos and augment them with thousands of annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE).
RobustTP is an approach that first computes trajectories using a combination of a non-linear motion model and a deep learning-based instance segmentation algorithm.
We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos.
We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories.
Ranked #1 on Trajectory Prediction on NGSIM