Adaptive Crowd Segmentation Based on Coherent Motion Detection

Fan, Zheyi; Jiang, Jiaojiao; Weng, Shuqin; He, Zhonghang; Liu, Zhiwen

doi:10.1007/s11265-017-1309-8

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Trojanova et al [12] adopted a weighted k-NN graph-based clustering approach, which used a data-driven threshold as compared to a static threshold in [7]. Fan et al [13] used a Natural Nearest Neighbour algorithm (3N) to adaptively determine the optimal number of the nearest neighbours (the k-value) as compared to k-NN based approach where the k-value needs to be experimentally determined. In their work, the 3N algorithm generates a crowd motion network from which similar motion patterns are detected using the concept of coherent neighbour invariance.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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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 knearest 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 paper, 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.

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Section: Related Workmentioning

confidence: 99%

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

2020

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“…Among the nonflow-based methods, the most representative are dynamic texture-based methods [27][28][29][30] and trackletbased methods. [31][32][33][34] Dynamic texture-based methods first model the moving crowd as a dynamic texture 35 with spatiotemporal statistical properties, and then use the matching among model parameters to perform crowd motion segmentation or abnormal behavior detection. As current dynamic texture models are relatively simple (such as linear dynamic system), crowd motion segmentation methods based on dynamic texture are currently only applicable to crowds with relatively simple movement modes and medium/lowdensities.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method is not suitable for short-term motion segmentation owing to its requirement for long-term trajectories of the keypoints in the crowd. Zhou et al 31,32 and Fan et al 33 utilized coherent filtering (CF) based on coherent neighbor invariance to perform short-term crowd motion segmentation via the local spatiotemporal relationships and motion correlations among tracklets. However, because coherent neighbor invariance primarily focuses on pairwise motion consistency and ignores the motion difference among all the keypoints in the local region of the center point, undersegmentation occurs easily in crowds with numerous motion patterns.…”

Section: Introductionmentioning

confidence: 99%

Translation domain segmentation model based on improved cosine similarity for crowd motion segmentation

Pan

Zhang

et al. 2019

J. Electron. Imag.

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With the continuous growth of the global population, large-scale public gatherings have become more common, and crowd management at these gatherings has become an urgent problem for public safety management. Crowd motion analysis and early warning based on crowd motion segmentation in video surveillance systems has become an important research topic in computer vision. A translation domain segmentation (TDS) model based on improved cosine similarity (ICS) is proposed to segment moving crowds with different crowding levels and complex motion modes. The method reconstructs the objective function of the basic TDS model by ICS to simultaneously measure the difference between both magnitude and direction of two vectors; thus, undersegmentation due to the magnitude difference can be avoided. By switching between "localization" and "globalization" modes of the objective function, the algorithm can be applied to segment crowds with different densities and motion states. Moreover, by simultaneously introducing local motion magnitude and local frame difference magnitude thresholds, nonforeground regions can be excluded from the initial regions during region evolution. Experimental results show that the proposed method achieves superior performance and higher accuracy compared to existing flow field-based methods when applied to complex scenes containing moving crowds.

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