Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering

Pai, Abhilash K and Karunakar, A K and Raghavendra, U (2020) Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering. IEEE Access, 8. ISSN 2169-3536

[img] PDF
10956.pdf - Published Version
Restricted to Registered users only

Download (4MB) | Request a copy


Motion pattern segmentation for crowded video scenes is an open problem because of the inability of existing approaches to tackle unpredictable crowd behaviour across varied scenes. To address this problem, we propose a Spatio-Angular Density-based Clustering (SADC) approach, which performs motion pattern segmentation by clustering the spatial and angular information obtained from the input trajectories. The k-nearest neighbours of each trajectory and the angular deviation between trajectories constitute the spatial and angular information, respectively. Effective integration of the spatio-angular information with an improvised density-based clustering algorithm makes this approach scene-independent. The performance of most clustering algorithms in the literature is parameter-driven. Choosing a single parameter value for different types of scenes decreases the overall clustering performance. In this article, we have shown that our approach is robust to scene changes using a single threshold, and, through the analysis of parameters across eight commonly occurring crowded scenarios, we point out the range of thresholds that are suitable for each scene category. We evaluate the proposed approach on the benchmarked CUHK dataset. The experimental results show the superior clustering performance and execution speed of the proposed approach when compared to the state-of-the-art over different scene categories.

Item Type: Article
Uncontrolled Keywords: Clustering, crowd analysis, crowd behaviour analysis, crowd flow segmentation, group detection, motion pattern segmentation
Subjects: Engineering > MIT Manipal > Instrumentation and Control
Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 08 Feb 2021 09:29
Last Modified: 08 Feb 2021 09:29

Actions (login required)

View Item View Item