Volume 15, Issue 2 (June 2019)                   IJEEE 2019, 15(2): 203-210 | Back to browse issues page

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Ahmadi P, Gholampour I. Efficient Analysis of Traffic Intersection Scenes by Employing Traffic Phase Information. IJEEE 2019; 15 (2) :203-210
URL: http://ijeee.iust.ac.ir/article-1-1080-en.html
Efficient Analysis of Traffic Intersection Scenes by Employing Traffic Phase Information
P. Ahmadi , I. Gholampour
Abstract:   (3439 Views)
Analyzing motion patterns in traffic videos can be employed directly to generate high-level descriptions of their content. For traffic videos captured from intersections, usually, we can easily provide additional information about traffic phases. Such information can be obtained directly from the traffic lights or through traffic lights controllers. In this paper, we focus on incorporating additional information to analyze the traffic videos more efficiently. Using side information on traffic phases, the semantic of motion patterns from traffic intersection scenes can be learned more effectively. The learning is performed based on optical flow features extracted from training video clips, and applying them to supervised topic models such as MedLDA and MedSTC. Based on such models, any video clip can be represented based on the learned patterns. Such representations can be further exploited in scene analysis, rule mining, abnormal event detection, etc. Our experiments show that employing side information in intersection video analysis leads to improvement in discovering scene pattern. Moreover, supervised topic models achieve about 4% improvement in abnormal event detection, compared to the unsupervised ones, in terms of area under ROC.
Keywords: Motion Patterns , Traffic Intersection , Traffic Phase , Supervised Topic Models
Full-Text [PDF 970 kb]   (1269 Downloads)    
Type of Study: Research Paper | Subject: ArtificialIntelligence
Received: 2017/04/15 | Revised: 2019/04/07 | Accepted: 2018/06/24
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.