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pro vyhledávání: '"Junaid Ahmed Ansari"'
Autor:
K. Madhava Krishna, Krishna Murthy Jatavallabhula, Junaid Ahmed Ansari, Anirudha Ramesh, Swapnil Daga, Rahul Sajnani, Gokul B. Nair
In this paper, we tackle the problem of multibody SLAM from a monocular camera. The term multibody, implies that we track the motion of the camera, as well as that of other dynamic participants in the scene. The quintessential challenge in dynamic sc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5276c68002c1d68ef63a3a0c82ca5019
Publikováno v:
IROS
We present a simple, fast, and light-weight RNN based framework for forecasting future locations of humans in first person monocular videos. The primary motivation for this work was to design a network which could accurately predict future trajectori
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60da8693620c4f8f2de42b07412da118
Autor:
Shashank Srikanth, J. Krishna Murthy, R. Karnik Ram, Sarthak Sharma, K. Madhava Krishna, Junaid Ahmed Ansari
Publikováno v:
IROS
In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representat
Publikováno v:
IEEE Transactions on Human-Machine Systems. 46:467-473
An open-source voice command interface kit has been developed by integrating open-source software and inexpensive hardware components. The software for the kit has been developed as a lightweight modular framework with replaceable components. It also
Publikováno v:
ICRA
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose,
Autor:
J. Krishna Murthy, Sarthak Sharma, Anshuman Majumdar, Junaid Ahmed Ansari, K. Madhava Krishna
Publikováno v:
IROS
Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e34a5fa575a92a029f543715cbe7211