Paper Info
Title
Speed Estimation and Abnormality Detection From Surveillance Cameras.
Abstract
Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.
Year
DOI
Venue
2018
10.1109/CVPRW.2018.00020
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Field
DocType
ISSN
Computer vision,Traffic analysis,Pattern recognition,Fisher vector,Computer science,Camera resectioning,Artificial intelligence,Abnormality detection,Optical flow,Calibration,Trajectory
Conference
2160-7508
Citations 
PageRank 
References 
1
0.34
0
Authors
6
Name
Order
Citations
PageRank
Panagiotis Giannakeris121.37
Vagia Kaltsa2382.23
Konstantinos Avgerinakis36214.10
A. Briassouli41448.00
Stefanos Vrochidis526373.19
Ioannis Kompatsiaris61404197.36