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Title: Temporal Unknown Incremental Clustering Model for Analysis of Traffic Surveillance Videos
Authors: Santhosh K.K.
Dogra D.P.
Pratim Roy, Partha
Published in: IEEE Transactions on Intelligent Transportation Systems
Abstract: Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling-based heuristic model referred to as temporal unknown incremental clustering has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to associate pixels belonging to a similar cluster in subsequent frames. The algorithm is fast and produces accurate results in ? (kn) time, where k is the number of clusters and n the number of pixels. Our experimental validation with publicly available data sets reveals that the proposed framework has good potential to open up new opportunities for real-time traffic analysis. © 2000-2011 IEEE.
Citation: IEEE Transactions on Intelligent Transportation Systems (2019), 20(5): 1762-1773
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Bayesian inference
Dirichlet process
Gibbs sampling
incremental clustering
real-time event detection
ISSN: 15249050
Author Scopus IDs: 56525012800
Author Affiliations: Santhosh, K.K., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 752050, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 752050, India
Roy, P.P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, 247667, India
Corresponding Author: Dogra, D.P.; School of Electrical Sciences, IIT BhubaneswarIndia; email:
Appears in Collections:Journal Publications [CS]

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