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Abstract: Depth estimation algorithms are useful components of computer vision systems to assess video streams on urban streets. They can provide important information about the street space and improve situational awareness for humans using the street space.
Faster Depth Estimation for Situational Awareness on Urban Streets
scholarworks.sjsu.edu › etd_theses
To provide situational awareness to humans, the inference time needs to be small, so that results are fresh and meaningful. This paper explores approaches like ...
Faster Depth Estimation for Situational Awareness on Urban Streets
www.computer.org › csdl › big-data
This paper explores two promising approaches - pruning and quantization - to improve inference time with a little compromise on the performance. We explore the ...
It is observed that using both pruning and quantization can improve the inference time dramatically, with a 39.6% speedup in inference time and an 81.2% ...
Abstract—Depth estimation algorithms are useful components of computer vision systems to assess video streams on urban streets. They can provide important ...
License. Faster depth Estimation for Situational Awareness on Urban Streets. Refer monodepth2 readme for basic instructions. Procedure -. First train model ...
... depth estimation using self-attention and discrete disparity volume. Python 62 7 · Faster-Depth-Estimation-for-Situational-Awareness-on-Urban-Streets Faster- ...
To the best of the authors' knowledge, this paper demonstrates real-time monocular depth estimation using a deep neural network with the highest throughput on ...
Missing: Situational Awareness Urban Streets.
... Awareness in Scheduling and Rescheduling Tasks for Novice Schedulers, Megan ... Faster Depth Estimation for Situational Awareness on Urban Streets, Sanjana ...