Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/342561
Title: | Adaptive clustering and motion flow vector techniques for detection of abnormalities in traffic video surveillance |
Researcher: | Joshan Athanesious J |
Guide(s): | Vasuhi S and Vaidehi V |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems Video Surveillance Detection of Abnormalities in Traffic Density Based Clustering Motion Flow Vector Techniques |
University: | Anna University |
Completed Date: | 2020 |
Abstract: | Detection of abnormal events in a traffic scene is a highly challenging task due to vast field of view, continuous stream of video data, various object interactions and complex events in Video Surveillance. Hence, this research proposes novel schemes using machine learning approach to detect abnormal events such as illegal U-turn, presence of pedestrian in driving region, wrong side driving and frequent lane change. newlineThe existing Density Based Clustering approach used for Anomaly detection in traffic scene uses random selection of cluster radius (Eps) and minimum points (minpts) needed to form a cluster. This random selection is time consuming and inefficient clustering results in accuracy reduction in abnormal detection. So, Adaptive Density based Spatial Clustering of Applications with Noise (ADBSCAN) is proposed for the detection of abnormal events based on spatial temporal information relating to individual objects which determines the optimal values for the cluster radius (Eps) using the slope calculation of the K-d plot. Gaussion Mixture Model (GMM) is used for obtaining the moving foreground regions and region-based tracking is used for the identification of the objects in successive frames. The centroid of the region is calculated using image moments. If there is an occlusion between the vehicles then vehicle identification number (Id no) is used to differentiate them. The main advantage in this technique is clustering / labelling the normal pattern without the help of manual intervention. newline newline |
Pagination: | xvii, 124p. |
URI: | http://hdl.handle.net/10603/342561 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 25.24 kB | Adobe PDF | View/Open |
02_certificates.pdf | 217.74 kB | Adobe PDF | View/Open | |
03_abstracts.pdf | 196.86 kB | Adobe PDF | View/Open | |
03_acknowledgements.pdf | 466.29 kB | Adobe PDF | View/Open | |
05_contents.pdf | 127.04 kB | Adobe PDF | View/Open | |
06_listoftables.pdf | 12.12 kB | Adobe PDF | View/Open | |
07_listoffigures.pdf | 239.29 kB | Adobe PDF | View/Open | |
08_listofabbreviations.pdf | 266.42 kB | Adobe PDF | View/Open | |
09_chapter1.pdf | 230.44 kB | Adobe PDF | View/Open | |
10_chapter2.pdf | 678.71 kB | Adobe PDF | View/Open | |
11_chapter3.pdf | 1.37 MB | Adobe PDF | View/Open | |
12_chapter4.pdf | 1.61 MB | Adobe PDF | View/Open | |
13_chapter5.pdf | 1.65 MB | Adobe PDF | View/Open | |
14_conclusion.pdf | 312.05 kB | Adobe PDF | View/Open | |
15_references.pdf | 180.28 kB | Adobe PDF | View/Open | |
16_listofpublications.pdf | 125.47 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 84.65 kB | Adobe PDF | View/Open |
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