Search Results for author:
Found 5 papers, 2 papers with code
ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
Enterprises are increasingly deploying large camera networks for video analytics.
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video
Models optimized for accuracy on single images are often prohibitively slow to run on each frame in a video.
Scaling Video Analytics Systems to Large Camera Deployments
Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises.
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame.
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation
Convolutional networks optimized for accuracy on challenging, dense prediction tasks are prohibitively slow to run on each frame in a video.
