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Found 12 papers, 2 papers with code
Scalable and Efficient Hierarchical Visual Topological Mapping
Based on our empirical analysis of multiple runs, we identify that continuity and distinctiveness are crucial characteristics for an optimal global descriptor that enable efficient and scalable hierarchical mapping, and present a methodology for quantifying and contrasting these characteristics across different global descriptors.
Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems.
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions.
Fast and Efficient Scene Categorization for Autonomous Driving using VAEs
We train a Variational Autoencoder in an unsupervised manner and map images to a constrained multi-dimensional latent space and use the latent vectors as compact embeddings that serve as global descriptors for images.
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation
The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
Ranked #18 on
Pose Estimation
on COCO test-dev
2.5D Vehicle Odometry Estimation
We show, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods.
A 2.5D Vehicle Odometry Estimation for Vision Applications
This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems.
Computer vision in automated parking systems: Design, implementation and challenges
In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms.
Vision-based Driver Assistance Systems: Survey, Taxonomy and Advances
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving.
Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras
In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization.
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture.
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications.
