Abstract
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.
Highlights
Pedestrians in a crowded scene exhibit interesting patterns of motion over time
SPATIO-ANGULAR DENSITY-BASED CLUSTERING (SADC) FOR MOTION PATTERN SEGMENTATION This paper considers scenarios involving sparse to dense crowds in shopping malls, train stations, escalators, street/sidewalk/market, crosswalks, road-traffic, marathon, military-parade, public events, other indoor and outdoor scenes which are under surveillance for crowd management using static cameras
The dataset contains a total of 474 videos captured from 215 scenes out of which 300 videos are used for the purpose of motion pattern segmentation
Summary
Pedestrians in a crowded scene exhibit interesting patterns of motion over time. Analysing these motion patterns across different types of crowded scenes helps to understand complex crowd behaviours, detect anomalous behaviours and predict unforeseen events which could pose a threat to the safety of the crowd.Motion pattern segmentation is an automated visual surveillance task that divides a scene into regions of consistent and coherent motion. Pedestrians in a crowded scene exhibit interesting patterns of motion over time. Analysing these motion patterns across different types of crowded scenes helps to understand complex crowd behaviours, detect anomalous behaviours and predict unforeseen events which could pose a threat to the safety of the crowd. In spite of various efforts [5]–[10], precise motion pattern segmentation remains a challenging task due to varying crowd dynamics across different types of scenes and intricate interactions between the pedestrians within a scene. In a structured crowded scene, the direction of motion of the crowd remains same for most of the time and is predictable. Most of the attempts to tackle the problem of motion pattern segmentation perform less efficiently when the type of scene changes, which results in wrongly detected segments
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